Tag: chatgpt

Agentic Architecture Pattern: Causality Based


    """
Pattern 159: Causality Based Pattern

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

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


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


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


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


class CausalityBasedPattern:
    """
    Causality Based Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for causality based
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: CausalityBasedConfig = None):
        self.config = config or CausalityBasedConfig()
        self.agents: Dict[str, CausalityBasedAgent] = {}
        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 Causality Based Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: CausalityBasedAgent):
        """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": "Causality Based Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Causality Based Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Causality Based Pattern Demo ===\n")
    
    # Create configuration
    config = CausalityBasedConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = CausalityBasedPattern(config)
    
    # Create and register agents
    agent1 = CausalityBasedAgent("Agent1", config)
    agent2 = CausalityBasedAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate causality based 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: Causality Based

Pattern 159: 159 Causality Based Pattern – Line-by-Line Explanation

Overview

Pattern 159: Causality Based Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 159
Pattern Name: 159 Causality Based Pattern
File Path: patterns\hybrid_ai\159_causality_based_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 159: Causality Based Pattern

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

Explanation: This module docstring describes the purpose and key features of the 159 Causality Based Pattern pattern.

Imports Section

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

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

Classes

CausalityBasedConfig (Line 18)

Description: Configuration for Causality Based Pattern.

CausalityBasedState (Line 27)

Description: State information for Causality Based Pattern.

CausalityBasedAgent (Line 36)

Description: Agent implementing Causality Based Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

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

Purpose: Process input according to Causality Based Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

CausalityBasedPattern (Line 85)

Description: Causality Based Pattern – Enhanced implementation

This pattern provides comprehensive functionality for causality based 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 = CausalityBasedConfig(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: Interpretable Agent


    """
Pattern 158: Interpretable Agent Pattern

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

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


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


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


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


class InterpretableAgentPattern:
    """
    Interpretable Agent Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for interpretable agent
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: InterpretableAgentConfig = None):
        self.config = config or InterpretableAgentConfig()
        self.agents: Dict[str, InterpretableAgentAgent] = {}
        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 Interpretable Agent Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: InterpretableAgentAgent):
        """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": "Interpretable Agent Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Interpretable Agent Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


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

Agentic Architecture Pattern: Interpretable Agent

Pattern 158: 158 Interpretable Agent Pattern – Line-by-Line Explanation

Overview

Pattern 158: Interpretable Agent Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 158
Pattern Name: 158 Interpretable Agent Pattern
File Path: patterns\hybrid_ai\158_interpretable_agent_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 158: Interpretable Agent Pattern

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

Explanation: This module docstring describes the purpose and key features of the 158 Interpretable Agent Pattern pattern.

Imports Section

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

Classes

InterpretableAgentConfig (Line 18)

Description: Configuration for Interpretable Agent Pattern.

InterpretableAgentState (Line 27)

Description: State information for Interpretable Agent Pattern.

InterpretableAgentAgent (Line 36)

Description: Agent implementing Interpretable Agent Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

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

Purpose: Process input according to Interpretable Agent Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

InterpretableAgentPattern (Line 85)

Description: Interpretable Agent Pattern – Enhanced implementation

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

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

Methods:

`init` (Line 99)

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

Purpose: No method docstring.

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

`_build_workflow` (Line 105)

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

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

`register_agent` (Line 150)

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

Purpose: Register an agent with the pattern.

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

`execute` (Line 156)

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

Purpose: Execute the pattern with given input.

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

`run` (Line 188)

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

Purpose: Main execution method.

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

`get_statistics` (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = InterpretableAgentConfig(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: Transparent Agent


    """
Pattern 157: Transparent Agent Pattern

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

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


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


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


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


class TransparentAgentPattern:
    """
    Transparent Agent Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for transparent agent
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: TransparentAgentConfig = None):
        self.config = config or TransparentAgentConfig()
        self.agents: Dict[str, TransparentAgentAgent] = {}
        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 Transparent Agent Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: TransparentAgentAgent):
        """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": "Transparent Agent Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Transparent Agent Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


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

Agentic Architecture Pattern: Transparent Agent

Pattern 157: 157 Transparent Agent Pattern – Line-by-Line Explanation

Overview

Pattern 157: Transparent Agent Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 157
Pattern Name: 157 Transparent Agent Pattern
File Path: patterns\hybrid_ai\157_transparent_agent_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 157: Transparent Agent Pattern

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

Explanation: This module docstring describes the purpose and key features of the 157 Transparent Agent Pattern pattern.

Imports Section

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

Classes

TransparentAgentConfig (Line 18)

Description: Configuration for Transparent Agent Pattern.

TransparentAgentState (Line 27)

Description: State information for Transparent Agent Pattern.

TransparentAgentAgent (Line 36)

Description: Agent implementing Transparent Agent Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

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

Purpose: Process input according to Transparent Agent Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

TransparentAgentPattern (Line 85)

Description: Transparent Agent Pattern – Enhanced implementation

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

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

Methods:

`init` (Line 99)

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

Purpose: No method docstring.

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

`_build_workflow` (Line 105)

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

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

`register_agent` (Line 150)

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

Purpose: Register an agent with the pattern.

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

`execute` (Line 156)

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

Purpose: Execute the pattern with given input.

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

`run` (Line 188)

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

Purpose: Main execution method.

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

`get_statistics` (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = TransparentAgentConfig(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: Explainable Agent


    """
Pattern 156: Explainable Agent Pattern

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

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


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


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


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


class ExplainableAgentPattern:
    """
    Explainable Agent Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for explainable agent
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: ExplainableAgentConfig = None):
        self.config = config or ExplainableAgentConfig()
        self.agents: Dict[str, ExplainableAgentAgent] = {}
        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 Explainable Agent Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: ExplainableAgentAgent):
        """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": "Explainable Agent Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Explainable Agent Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


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

Agentic Architecture Pattern: Explainable Agent

Pattern 156: 156 Explainable Agent Pattern – Line-by-Line Explanation

Overview

Pattern 156: Explainable Agent Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 156
Pattern Name: 156 Explainable Agent Pattern
File Path: patterns\hybrid_ai\156_explainable_agent_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 156: Explainable Agent Pattern

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

Explanation: This module docstring describes the purpose and key features of the 156 Explainable Agent Pattern pattern.

Imports Section

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

Classes

ExplainableAgentConfig (Line 18)

Description: Configuration for Explainable Agent Pattern.

ExplainableAgentState (Line 27)

Description: State information for Explainable Agent Pattern.

ExplainableAgentAgent (Line 36)

Description: Agent implementing Explainable Agent Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

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

Purpose: Process input according to Explainable Agent Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

ExplainableAgentPattern (Line 85)

Description: Explainable Agent Pattern – Enhanced implementation

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

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

Methods:

`init` (Line 99)

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

Purpose: No method docstring.

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

`_build_workflow` (Line 105)

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

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

`register_agent` (Line 150)

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

Purpose: Register an agent with the pattern.

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

`execute` (Line 156)

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

Purpose: Execute the pattern with given input.

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

`run` (Line 188)

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

Purpose: Main execution method.

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

`get_statistics` (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = ExplainableAgentConfig(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: Human Agent Collaboration


    """
Pattern 155: Human-Agent Collaboration Pattern
Purpose: Seamless collaboration between humans and AI agents
"""

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


class CollaborationMode(Enum):
    """Modes of human-agent collaboration."""
    HUMAN_LED = "human_led"
    AGENT_LED = "agent_led"
    EQUAL_PARTNERSHIP = "equal_partnership"
    HUMAN_OVERSIGHT = "human_oversight"


@dataclass
class CollaborationTask:
    """A collaborative task."""
    id: str
    description: str
    mode: CollaborationMode
    human_input: Optional[str] = None
    agent_output: Optional[str] = None
    human_feedback: Optional[str] = None
    iterations: int = 0
    completed: bool = False
    created_at: datetime = field(default_factory=datetime.now)


class HumanAgentCollaborationPattern:
    """
    Human-Agent Collaboration - Synergistic human-AI teamwork
    
    Combines human creativity, judgment, and domain expertise
    with AI's computational power, pattern recognition, and scalability.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.tasks: List[CollaborationTask] = []
        self.collaboration_history: List[Dict[str, Any]] = []
        
    def create_task(self, task_id: str, description: str, mode: CollaborationMode) -> CollaborationTask:
        """Create a collaborative task."""
        task = CollaborationTask(id=task_id, description=description, mode=mode)
        self.tasks.append(task)
        return task
    
    def agent_process(self, task: CollaborationTask, context: str = "") -> str:
        """Agent processes task."""
        mode_guidance = {
            CollaborationMode.HUMAN_LED: "Follow human guidance closely. Provide support.",
            CollaborationMode.AGENT_LED: "Take initiative. Present recommendations to human.",
            CollaborationMode.EQUAL_PARTNERSHIP: "Collaborate as equals. Share decision-making.",
            CollaborationMode.HUMAN_OVERSIGHT: "Work autonomously but seek human approval."
        }
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"Collaboration mode: {mode_guidance[task.mode]}"),
            ("human", "Task: {task}\nContext: {context}\nProvide your contribution:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"task": task.description, "context": context})
        
        task.agent_output = str(response.content)
        task.iterations += 1
        
        return task.agent_output
    
    def incorporate_human_input(self, task: CollaborationTask, human_input: str):
        """Incorporate human input into task."""
        task.human_input = human_input
        
        # Agent responds to human input
        if task.mode in [CollaborationMode.EQUAL_PARTNERSHIP, CollaborationMode.AGENT_LED]:
            self.agent_process(task, context=f"Human input: {human_input}")
    
    def incorporate_human_feedback(self, task: CollaborationTask, feedback: str):
        """Incorporate human feedback."""
        task.human_feedback = feedback
        
        # Refine based on feedback
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Refine your work based on human feedback"),
            ("human", "Previous output: {output}\nFeedback: {feedback}\nProvide improved version:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": task.agent_output,
            "feedback": feedback
        })
        
        task.agent_output = str(response.content)
        task.iterations += 1
    
    def complete_task(self, task: CollaborationTask):
        """Mark task as complete."""
        task.completed = True
        
        self.collaboration_history.append({
            "task_id": task.id,
            "mode": task.mode.value,
            "iterations": task.iterations,
            "human_involved": task.human_input is not None,
            "feedback_provided": task.human_feedback is not None,
            "completed_at": datetime.now()
        })
    
    def run(self, task_id: str, description: str, mode: CollaborationMode, 
            human_inputs: List[str] = None) -> Dict[str, Any]:
        """Execute collaborative workflow."""
        task = self.create_task(task_id, description, mode)
        
        # Initial agent processing
        agent_output = self.agent_process(task)
        
        # Incorporate human inputs if provided
        if human_inputs:
            for human_input in human_inputs:
                if "feedback:" in human_input.lower():
                    self.incorporate_human_feedback(task, human_input)
                else:
                    self.incorporate_human_input(task, human_input)
        
        self.complete_task(task)
        
        return {
            "pattern": "HumanAgentCollaboration",
            "task_id": task_id,
            "mode": mode.value,
            "iterations": task.iterations,
            "final_output": task.agent_output[:200] + "..." if len(task.agent_output) > 200 else task.agent_output,
            "human_involvement": len(human_inputs) if human_inputs else 0
        }
    
    def get_stats(self) -> Dict[str, Any]:
        """Get collaboration statistics."""
        if not self.collaboration_history:
            return {"no_data": True}
        
        return {
            "total_collaborations": len(self.collaboration_history),
            "avg_iterations": sum(h["iterations"] for h in self.collaboration_history) / len(self.collaboration_history),
            "human_involvement_rate": sum(1 for h in self.collaboration_history if h["human_involved"]) / len(self.collaboration_history),
            "feedback_rate": sum(1 for h in self.collaboration_history if h["feedback_provided"]) / len(self.collaboration_history)
        }


if __name__ == "__main__":
    print("=== Human-Agent Collaboration Pattern Demo ===\n")
    
    pattern = HumanAgentCollaborationPattern()
    
    # Demo 1: Equal Partnership
    print("Demo 1: Equal Partnership Mode")
    result1 = pattern.run(
        "collab1",
        "Design a new mobile app feature",
        CollaborationMode.EQUAL_PARTNERSHIP,
        human_inputs=[
            "I think we should focus on accessibility",
            "Feedback: Great ideas, but make it simpler"
        ]
    )
    print(f"Mode: {result1['mode']}")
    print(f"Iterations: {result1['iterations']}")
    print(f"Output: {result1['final_output']}\n")
    
    # Demo 2: Human Oversight
    print("Demo 2: Human Oversight Mode")
    result2 = pattern.run(
        "collab2",
        "Analyze quarterly financial data",
        CollaborationMode.HUMAN_OVERSIGHT,
        human_inputs=["Approved, proceed with recommendations"]
    )
    print(f"Mode: {result2['mode']}")
    print(f"Output: {result2['final_output'][:100]}...\n")
    
    print("="*70)
    stats = pattern.get_stats()
    print(f"Statistics: {stats}")

Agentic Architecture Pattern: Human Agent Collaboration

Pattern 155: 155 Human Agent Collaboration Pattern – Line-by-Line Explanation

Overview

Pattern 155: Human-Agent Collaboration Pattern Purpose: Seamless collaboration between humans and AI agents

Code Structure Analysis

File Information

Pattern Number: 155
Pattern Name: 155 Human Agent Collaboration Pattern
File Path: patterns\hybrid_ai\155_human_agent_collaboration_pattern.py
Total Lines: 191

Detailed Code Explanation

Module Docstring

"""
Pattern 155: Human-Agent Collaboration Pattern
Purpose: Seamless collaboration between humans and AI agents
"""

Explanation: This module docstring describes the purpose and key features of the 155 Human Agent Collaboration Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate  # 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_openai import ChatOpenAI: LangChain framework integration – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration

Enumerations

CollaborationMode (Line 14)

class CollaborationMode(Enum):
    HUMAN_LED
    AGENT_LED
    EQUAL_PARTNERSHIP
    HUMAN_OVERSIGHT

Purpose: Modes of human-agent collaboration.

Values: – HUMAN_LED: Represents human led – AGENT_LED: Represents agent led – EQUAL_PARTNERSHIP: Represents equal partnership – HUMAN_OVERSIGHT: Represents human oversight

Classes

CollaborationMode (Line 14)

Description: Modes of human-agent collaboration.

CollaborationTask (Line 23)

Description: A collaborative task.

HumanAgentCollaborationPattern (Line 36)

Description: Human-Agent Collaboration – Synergistic human-AI teamwork

Combines human creativity, judgment, and domain expertise with AI’s computational power, pattern recognition, and scalability.

Methods:

`init` (Line 44)

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”)

`create_task` (Line 49)

def create_task(self, task_id, description, mode):
    """Create a collaborative task."""

Purpose: Create a collaborative task.

Parameters: – self: Instance reference – task_id: Parameter for task_id – description: Parameter for description – mode: Parameter for mode

`agent_process` (Line 55)

def agent_process(self, task, context):
    """Agent processes task."""

Purpose: Agent processes task.

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

`incorporate_human_input` (Line 77)

def incorporate_human_input(self, task, human_input):
    """Incorporate human input into task."""

Purpose: Incorporate human input into task.

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

`incorporate_human_feedback` (Line 85)

def incorporate_human_feedback(self, task, feedback):
    """Incorporate human feedback."""

Purpose: Incorporate human feedback.

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

`complete_task` (Line 104)

def complete_task(self, task):
    """Mark task as complete."""

Purpose: Mark task as complete.

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

`run` (Line 117)

def run(self, task_id, description, mode, human_inputs):
    """Execute collaborative workflow."""

Purpose: Execute collaborative workflow.

Parameters: – self: Instance reference – task_id: Parameter for task_id – description: Parameter for description – mode: Parameter for mode – human_inputs: Parameter for human_inputs

`get_stats` (Line 144)

def get_stats(self):
    """Get collaboration statistics."""

Purpose: Get collaboration 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 = CollaborationMode(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: Human In The Loop


    """
Pattern 154: Human-in-the-Loop Pattern

Incorporates human feedback and decisions into agent workflows.
Implements interactive decision points with LangGraph state management.
"""

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


class ApprovalStatus(Enum):
    """Status of human approval."""
    PENDING = "pending"
    APPROVED = "approved"
    REJECTED = "rejected"
    MODIFIED = "modified"


class DecisionType(Enum):
    """Type of human decision required."""
    APPROVAL = "approval"
    SELECTION = "selection"
    CORRECTION = "correction"
    FEEDBACK = "feedback"


@dataclass
class HumanDecisionPoint:
    """Represents a point requiring human input."""
    decision_id: str
    decision_type: DecisionType
    question: str
    options: List[str] = field(default_factory=list)
    context: Dict[str, Any] = field(default_factory=dict)
    ai_recommendation: Optional[str] = None
    human_response: Optional[str] = None
    status: ApprovalStatus = ApprovalStatus.PENDING
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class WorkflowStep:
    """Step in the workflow."""
    step_id: str
    name: str
    requires_human: bool = False
    decision_type: Optional[DecisionType] = None
    completed: bool = False
    result: Optional[Any] = None


class HumanInLoopAgent:
    """
    Agent that integrates human decisions into its workflow.
    """
    
    def __init__(
        self,
        name: str,
        model_name: str = "gpt-4"
    ):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.decision_points: List[HumanDecisionPoint] = []
        self.workflow_steps: List[WorkflowStep] = []
        
    def request_human_decision(
        self,
        decision_type: DecisionType,
        question: str,
        options: List[str] = None,
        context: Dict[str, Any] = None,
        provide_ai_recommendation: bool = True
    ) -> HumanDecisionPoint:
        """Request a decision from human."""
        decision_id = f"decision-{len(self.decision_points) + 1}"
        
        # Generate AI recommendation
        ai_recommendation = None
        if provide_ai_recommendation:
            ai_recommendation = self._generate_recommendation(question, options, context)
        
        decision = HumanDecisionPoint(
            decision_id=decision_id,
            decision_type=decision_type,
            question=question,
            options=options or [],
            context=context or {},
            ai_recommendation=ai_recommendation
        )
        
        self.decision_points.append(decision)
        return decision
    
    def _generate_recommendation(
        self,
        question: str,
        options: List[str],
        context: Dict[str, Any]
    ) -> str:
        """Generate AI recommendation for human."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, an AI assistant. Provide a recommendation for the human."),
            ("human", """Question: {question}

Options:
{options}

Context:
{context}

Provide your recommendation and brief reasoning:""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "question": question,
            "options": "\n".join([f"- {opt}" for opt in options]) if options else "No specific options",
            "context": "\n".join([f"{k}: {v}" for k, v in (context or {}).items()])
        })
        
        return str(response.content)
    
    def receive_human_feedback(
        self,
        decision_id: str,
        response: str,
        status: ApprovalStatus = ApprovalStatus.APPROVED
    ):
        """Receive human feedback for a decision."""
        for decision in self.decision_points:
            if decision.decision_id == decision_id:
                decision.human_response = response
                decision.status = status
                break
    
    def execute_with_human_oversight(
        self,
        task: str,
        steps: List[WorkflowStep],
        human_input_func: Callable[[HumanDecisionPoint], str]
    ) -> Dict[str, Any]:
        """Execute workflow with human oversight."""
        self.workflow_steps = steps
        results = []
        
        for step in steps:
            if step.requires_human:
                # Request human decision
                decision = self.request_human_decision(
                    decision_type=step.decision_type or DecisionType.APPROVAL,
                    question=f"Approve step: {step.name}?",
                    context={"step": step.name, "task": task}
                )
                
                # Get human input (simulated)
                human_response = human_input_func(decision)
                
                # Process response
                self.receive_human_feedback(
                    decision.decision_id,
                    human_response,
                    ApprovalStatus.APPROVED if "yes" in human_response.lower() else ApprovalStatus.REJECTED
                )
                
                if decision.status == ApprovalStatus.APPROVED:
                    result = self._execute_step(step, task)
                    results.append(result)
                else:
                    results.append({
                        "step": step.name,
                        "status": "rejected",
                        "reason": human_response
                    })
                    break
            else:
                # Execute without human input
                result = self._execute_step(step, task)
                results.append(result)
        
        return {
            "task": task,
            "results": results,
            "decisions_count": len(self.decision_points),
            "steps_completed": len([r for r in results if r.get("status") != "rejected"])
        }
    
    def _execute_step(self, step: WorkflowStep, task: str) -> Dict[str, Any]:
        """Execute a workflow step."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}. Execute the workflow step."),
            ("human", "Task: {task}\nStep: {step_name}\n\nExecute this step and provide the result:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "task": task,
            "step_name": step.name
        })
        
        step.completed = True
        step.result = str(response.content)
        
        return {
            "step": step.name,
            "status": "completed",
            "result": step.result
        }
    
    def get_decision_summary(self) -> Dict[str, Any]:
        """Get summary of human decisions."""
        if not self.decision_points:
            return {"total": 0}
        
        return {
            "total": len(self.decision_points),
            "approved": len([d for d in self.decision_points if d.status == ApprovalStatus.APPROVED]),
            "rejected": len([d for d in self.decision_points if d.status == ApprovalStatus.REJECTED]),
            "pending": len([d for d in self.decision_points if d.status == ApprovalStatus.PENDING]),
            "decisions": [
                {
                    "id": d.decision_id,
                    "type": d.decision_type.value,
                    "question": d.question,
                    "ai_recommendation": d.ai_recommendation,
                    "human_response": d.human_response,
                    "status": d.status.value
                }
                for d in self.decision_points
            ]
        }


class HumanInLoopPattern:
    """
    Complete Human-in-the-Loop pattern with LangGraph workflow.
    """
    
    def __init__(self, agent: HumanInLoopAgent):
        self.agent = agent
        self.workflow = self._create_workflow()
        
    def _create_workflow(self) -> StateGraph:
        """Create workflow with human decision points."""
        
        class HumanLoopState(TypedDict):
            task: str
            current_step: int
            steps: List[Dict[str, Any]]
            results: List[Dict[str, Any]]
            human_decisions: List[Dict[str, Any]]
        
        workflow = StateGraph(HumanLoopState)
        
        def execute_step(state: HumanLoopState) -> HumanLoopState:
            """Execute current step."""
            if state["current_step"]  str:
            """Check if workflow should continue."""
            return "execute" if state["current_step"]  Dict[str, Any]:
        """Run workflow with human-in-the-loop."""
        return self.agent.execute_with_human_oversight(task, steps, human_input_func)


def main():
    """Example usage of Human-in-the-Loop Pattern."""
    print("=" * 80)
    print("Human-in-the-Loop Pattern Demo")
    print("=" * 80)
    
    # Create agent
    agent = HumanInLoopAgent(name="ContentAgent")
    pattern = HumanInLoopPattern(agent)
    
    # Define workflow steps
    steps = [
        WorkflowStep(
            step_id="step-1",
            name="Generate content outline",
            requires_human=False
        ),
        WorkflowStep(
            step_id="step-2",
            name="Review and approve outline",
            requires_human=True,
            decision_type=DecisionType.APPROVAL
        ),
        WorkflowStep(
            step_id="step-3",
            name="Write full content",
            requires_human=False
        ),
        WorkflowStep(
            step_id="step-4",
            name="Review and approve final content",
            requires_human=True,
            decision_type=DecisionType.APPROVAL
        ),
        WorkflowStep(
            step_id="step-5",
            name="Publish content",
            requires_human=False
        )
    ]
    
    # Simulate human input function
    def simulated_human_input(decision: HumanDecisionPoint) -> str:
        """Simulate human providing input."""
        print(f"\n{'=' * 80}")
        print(f"🤔 Human Decision Required")
        print(f"{'=' * 80}")
        print(f"Question: {decision.question}")
        print(f"Type: {decision.decision_type.value}")
        
        if decision.ai_recommendation:
            print(f"\n💡 AI Recommendation:")
            print(f"{decision.ai_recommendation}")
        
        # Simulate approval (in real scenario, get actual input)
        import random
        response = "yes, approved" if random.random() > 0.2 else "no, needs revision"
        print(f"\n✅ Human Response: {response}")
        
        return response
    
    # Run workflow
    task = "Create blog post about AI agents"
    
    print(f"\n📝 Task: {task}")
    print(f"Steps: {len(steps)}")
    
    print("\n" + "=" * 80)
    print("🔄 Executing Workflow with Human Oversight")
    print("=" * 80)
    
    result = pattern.run(task, steps, simulated_human_input)
    
    # Display results
    print("\n" + "=" * 80)
    print("✅ Workflow Results")
    print("=" * 80)
    print(f"Task: {result['task']}")
    print(f"Steps Completed: {result['steps_completed']}/{len(steps)}")
    print(f"Human Decisions: {result['decisions_count']}")
    
    print("\nStep Results:")
    for i, res in enumerate(result["results"], 1):
        print(f"\n{i}. {res['step']}")
        print(f"   Status: {res['status']}")
        if res.get('result'):
            print(f"   Result: {str(res['result'])[:100]}...")
    
    # Display decision summary
    print("\n" + "=" * 80)
    print("📊 Decision Summary")
    print("=" * 80)
    
    summary = agent.get_decision_summary()
    print(f"Total Decisions: {summary['total']}")
    print(f"Approved: {summary['approved']}")
    print(f"Rejected: {summary['rejected']}")
    print(f"Pending: {summary['pending']}")
    
    print("\nDecision Details:")
    for decision in summary["decisions"]:
        print(f"\n{decision['id']} ({decision['type']})")
        print(f"  Question: {decision['question']}")
        print(f"  Status: {decision['status']}")
        if decision['human_response']:
            print(f"  Response: {decision['human_response']}")


if __name__ == "__main__":
    main()

Agentic Architecture Pattern: Human In The Loop

Pattern 154: 154 Human In The Loop Pattern – Line-by-Line Explanation

Overview

Pattern 154: Human-in-the-Loop Pattern

Incorporate human feedback and oversight at critical decision points in the agent workflow. Humans can approve, reject, or modify agent actions.

Code Structure Analysis

File Information

Pattern Number: 154
Pattern Name: 154 Human In The Loop Pattern
File Path: patterns\hybrid_ai\154_human_in_the_loop_pattern.py
Total Lines: 242

Detailed Code Explanation

Module Docstring

"""
Pattern 154: Human-in-the-Loop Pattern

Incorporate human feedback and oversight at critical decision points in the
agent workflow. Humans can approve, reject, or modify agent actions.
"""

Explanation: This module docstring describes the purpose and key features of the 154 Human In The Loop Pattern pattern.

Imports Section

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

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

Enumerations

ApprovalStatus (Line 16)

class ApprovalStatus(Enum):
    PENDING
    APPROVED
    REJECTED
    MODIFIED

Purpose: Status of human approval.

Values: – PENDING: Initial state, not yet processed – APPROVED: Represents approved – REJECTED: Represents rejected – MODIFIED: Represents modified

Classes

ApprovalStatus (Line 16)

Description: Status of human approval.

HumanFeedback (Line 25)

Description: Human feedback on agent action.

AgentAction (Line 35)

Description: Action proposed by agent.

HumanInTheLoopPattern (Line 45)

Description: Human-in-the-Loop Pattern – Human oversight of agent actions.

Methods:

`init` (Line 50)

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

Purpose: No method docstring.

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

`set_feedback_function` (Line 57)

def set_feedback_function(self, func):
    """Set the function that gets human feedback (simulated or real)."""

Purpose: Set the function that gets human feedback (simulated or real).

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

`propose_action` (Line 61)

def propose_action(self, description, proposed_action, confidence):
    """Agent proposes an action."""

Purpose: Agent proposes an action.

Parameters: – self: Instance reference – description: Parameter for description – proposed_action: Parameter for proposed_action – confidence: Parameter for confidence

`get_human_feedback` (Line 79)

def get_human_feedback(self, action):
    """Get human feedback on an action."""

Purpose: Get human feedback on an action.

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

`execute_with_approval` (Line 113)

def execute_with_approval(self, action):
    """Execute action with human approval if needed."""

Purpose: Execute action with human approval if needed.

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

`run` (Line 149)

def run(self, task):
    """Execute task with human-in-the-loop."""

Purpose: Execute task with human-in-the-loop.

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

`get_statistics` (Line 183)

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

Purpose: Get HITL 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 = ApprovalStatus(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: Knowledge Guided


    """
Pattern 153: Knowledge Guided Pattern

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

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


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


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


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


class KnowledgeGuidedPattern:
    """
    Knowledge Guided Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for knowledge guided
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: KnowledgeGuidedConfig = None):
        self.config = config or KnowledgeGuidedConfig()
        self.agents: Dict[str, KnowledgeGuidedAgent] = {}
        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 Knowledge Guided Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: KnowledgeGuidedAgent):
        """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": "Knowledge Guided Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Knowledge Guided Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Knowledge Guided Pattern Demo ===\n")
    
    # Create configuration
    config = KnowledgeGuidedConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = KnowledgeGuidedPattern(config)
    
    # Create and register agents
    agent1 = KnowledgeGuidedAgent("Agent1", config)
    agent2 = KnowledgeGuidedAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate knowledge guided 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: Knowledge Guided

Pattern 153: 153 Knowledge Guided Pattern – Line-by-Line Explanation

Overview

Pattern 153: Knowledge Guided Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 153
Pattern Name: 153 Knowledge Guided Pattern
File Path: patterns\hybrid_ai\153_knowledge_guided_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 153: Knowledge Guided Pattern

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

Explanation: This module docstring describes the purpose and key features of the 153 Knowledge Guided Pattern pattern.

Imports Section

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

Classes

KnowledgeGuidedConfig (Line 18)

Description: Configuration for Knowledge Guided Pattern.

KnowledgeGuidedState (Line 27)

Description: State information for Knowledge Guided Pattern.

KnowledgeGuidedAgent (Line 36)

Description: Agent implementing Knowledge Guided Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

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

Purpose: Process input according to Knowledge Guided Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

KnowledgeGuidedPattern (Line 85)

Description: Knowledge Guided Pattern – Enhanced implementation

This pattern provides comprehensive functionality for knowledge guided 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 = KnowledgeGuidedConfig(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: Neuro Symbolic


    """
Pattern 152: Neuro Symbolic Pattern

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

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


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


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


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


class NeuroSymbolicPattern:
    """
    Neuro Symbolic Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for neuro symbolic
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: NeuroSymbolicConfig = None):
        self.config = config or NeuroSymbolicConfig()
        self.agents: Dict[str, NeuroSymbolicAgent] = {}
        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 Neuro Symbolic Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: NeuroSymbolicAgent):
        """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": "Neuro Symbolic Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Neuro Symbolic Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Neuro Symbolic Pattern Demo ===\n")
    
    # Create configuration
    config = NeuroSymbolicConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = NeuroSymbolicPattern(config)
    
    # Create and register agents
    agent1 = NeuroSymbolicAgent("Agent1", config)
    agent2 = NeuroSymbolicAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate neuro symbolic 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: Neuro Symbolic

Pattern 152: 152 Neuro Symbolic Pattern – Line-by-Line Explanation

Overview

Pattern 152: Neuro Symbolic Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 152
Pattern Name: 152 Neuro Symbolic Pattern
File Path: patterns\hybrid_ai\152_neuro_symbolic_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 152: Neuro Symbolic Pattern

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

Explanation: This module docstring describes the purpose and key features of the 152 Neuro Symbolic Pattern pattern.

Imports Section

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

Classes

NeuroSymbolicConfig (Line 18)

Description: Configuration for Neuro Symbolic Pattern.

NeuroSymbolicState (Line 27)

Description: State information for Neuro Symbolic Pattern.

NeuroSymbolicAgent (Line 36)

Description: Agent implementing Neuro Symbolic Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

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

Purpose: Process input according to Neuro Symbolic Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

NeuroSymbolicPattern (Line 85)

Description: Neuro Symbolic Pattern – Enhanced implementation

This pattern provides comprehensive functionality for neuro symbolic 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 = NeuroSymbolicConfig(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: Symbolic Neural Hybrid


    """
Pattern 151: Symbolic-Neural Hybrid Pattern

Combines symbolic reasoning with neural networks for robust AI systems.
Integrates rule-based logic with LLM capabilities using LangChain.
"""

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


class ReasoningMode(Enum):
    """Mode of reasoning."""
    SYMBOLIC = "symbolic"
    NEURAL = "neural"
    HYBRID = "hybrid"


@dataclass
class Rule:
    """Symbolic rule for logical reasoning."""
    rule_id: str
    name: str
    conditions: List[str]  # Logical conditions
    conclusion: str
    confidence: float = 1.0


@dataclass
class Fact:
    """Fact in the knowledge base."""
    fact_id: str
    predicate: str
    arguments: List[str]
    confidence: float = 1.0
    source: str = "user"  # user, symbolic, neural, hybrid


@dataclass
class InferenceResult:
    """Result of inference."""
    conclusion: str
    mode: ReasoningMode
    confidence: float
    explanation: str
    supporting_facts: List[Fact] = field(default_factory=list)
    applied_rules: List[Rule] = field(default_factory=list)


class SymbolicReasoner:
    """Symbolic reasoning engine using rules."""
    
    def __init__(self):
        self.rules: Dict[str, Rule] = {}
        self.facts: Dict[str, Fact] = {}
        
    def add_rule(self, rule: Rule):
        """Add a reasoning rule."""
        self.rules[rule.rule_id] = rule
        
    def add_fact(self, fact: Fact):
        """Add a fact to knowledge base."""
        self.facts[fact.fact_id] = fact
        
    def infer(self, query: str) -> List[InferenceResult]:
        """Perform symbolic inference."""
        results = []
        
        # Find applicable rules
        for rule in self.rules.values():
            # Check if all conditions are satisfied
            satisfied = True
            supporting_facts = []
            
            for condition in rule.conditions:
                # Find matching facts
                matching = [f for f in self.facts.values() if condition in f.predicate]
                if matching:
                    supporting_facts.extend(matching)
                else:
                    satisfied = False
                    break
            
            if satisfied:
                # Calculate confidence
                if supporting_facts:
                    confidence = min(f.confidence for f in supporting_facts) * rule.confidence
                else:
                    confidence = rule.confidence
                
                result = InferenceResult(
                    conclusion=rule.conclusion,
                    mode=ReasoningMode.SYMBOLIC,
                    confidence=confidence,
                    explanation=f"Applied rule '{rule.name}' with {len(supporting_facts)} supporting facts",
                    supporting_facts=supporting_facts,
                    applied_rules=[rule]
                )
                results.append(result)
        
        return results


class NeuralReasoner:
    """Neural reasoning using LLM."""
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        
    def infer(self, query: str, context: Dict[str, Any]) -> InferenceResult:
        """Perform neural inference using LLM."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a reasoning AI. Analyze the query and context to draw conclusions."),
            ("human", """Query: {query}

Context:
{context}

Provide:
1. Your conclusion
2. Confidence (0.0 to 1.0)
3. Brief explanation

Format: CONCLUSION|confidence|explanation""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "query": query,
            "context": "\n".join([f"{k}: {v}" for k, v in context.items()])
        })
        
        # Parse response
        parts = str(response.content).split('|')
        conclusion = parts[0].strip() if len(parts) > 0 else "Unknown"
        confidence = float(parts[1].strip()) if len(parts) > 1 else 0.5
        explanation = parts[2].strip() if len(parts) > 2 else "LLM reasoning"
        
        return InferenceResult(
            conclusion=conclusion,
            mode=ReasoningMode.NEURAL,
            confidence=confidence,
            explanation=explanation
        )


class SymbolicNeuralHybrid:
    """
    Hybrid system combining symbolic and neural reasoning.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.symbolic = SymbolicReasoner()
        self.neural = NeuralReasoner(model_name=model_name)
        self.llm = ChatOpenAI(model=model_name, temperature=0.5)
        
    def reason(
        self,
        query: str,
        mode: ReasoningMode = ReasoningMode.HYBRID
    ) -> List[InferenceResult]:
        """Perform reasoning using specified mode."""
        if mode == ReasoningMode.SYMBOLIC:
            return self.symbolic.infer(query)
        
        elif mode == ReasoningMode.NEURAL:
            context = self._build_context()
            return [self.neural.infer(query, context)]
        
        elif mode == ReasoningMode.HYBRID:
            return self._hybrid_reasoning(query)
        
        return []
    
    def _hybrid_reasoning(self, query: str) -> List[InferenceResult]:
        """Combine symbolic and neural reasoning."""
        # First try symbolic reasoning
        symbolic_results = self.symbolic.infer(query)
        
        # Get neural reasoning
        context = self._build_context()
        context["query"] = query
        neural_result = self.neural.infer(query, context)
        
        # Combine results
        if symbolic_results:
            # Use LLM to synthesize both approaches
            synthesized = self._synthesize_results(query, symbolic_results, neural_result)
            return [synthesized] + symbolic_results + [neural_result]
        else:
            # Only neural result available
            return [neural_result]
    
    def _synthesize_results(
        self,
        query: str,
        symbolic_results: List[InferenceResult],
        neural_result: InferenceResult
    ) -> InferenceResult:
        """Synthesize symbolic and neural results."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a reasoning synthesizer. Combine symbolic and neural reasoning results."),
            ("human", """Query: {query}

Symbolic Reasoning:
{symbolic}

Neural Reasoning:
{neural}

Synthesize a final conclusion that combines both approaches.
Format: CONCLUSION|confidence|explanation""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "query": query,
            "symbolic": "\n".join([f"- {r.conclusion} (confidence: {r.confidence})" for r in symbolic_results]),
            "neural": f"{neural_result.conclusion} (confidence: {neural_result.confidence})"
        })
        
        # Parse response
        parts = str(response.content).split('|')
        conclusion = parts[0].strip() if len(parts) > 0 else "Hybrid conclusion"
        confidence = float(parts[1].strip()) if len(parts) > 1 else 0.7
        explanation = parts[2].strip() if len(parts) > 2 else "Combined symbolic and neural reasoning"
        
        return InferenceResult(
            conclusion=conclusion,
            mode=ReasoningMode.HYBRID,
            confidence=confidence,
            explanation=explanation,
            supporting_facts=symbolic_results[0].supporting_facts if symbolic_results else [],
            applied_rules=symbolic_results[0].applied_rules if symbolic_results else []
        )
    
    def _build_context(self) -> Dict[str, Any]:
        """Build context from knowledge base."""
        context = {}
        
        # Add facts
        if self.symbolic.facts:
            context["facts"] = [
                f"{f.predicate}({', '.join(f.arguments)})"
                for f in self.symbolic.facts.values()
            ]
        
        # Add rules
        if self.symbolic.rules:
            context["rules"] = [
                f"{r.name}: IF {' AND '.join(r.conditions)} THEN {r.conclusion}"
                for r in self.symbolic.rules.values()
            ]
        
        return context


class SymbolicNeuralHybridPattern:
    """Complete hybrid pattern implementation."""
    
    def __init__(self, model_name: str = "gpt-4"):
        self.hybrid = SymbolicNeuralHybrid(model_name=model_name)
        
    def run(
        self,
        query: str,
        mode: ReasoningMode = ReasoningMode.HYBRID
    ) -> Dict[str, Any]:
        """Run hybrid reasoning."""
        results = self.hybrid.reason(query, mode)
        
        return {
            "query": query,
            "mode": mode.value,
            "results": [
                {
                    "conclusion": r.conclusion,
                    "confidence": r.confidence,
                    "mode": r.mode.value,
                    "explanation": r.explanation,
                    "supporting_facts_count": len(r.supporting_facts),
                    "applied_rules_count": len(r.applied_rules)
                }
                for r in results
            ]
        }


def main():
    """Example usage of Symbolic-Neural Hybrid Pattern."""
    print("=" * 80)
    print("Symbolic-Neural Hybrid Pattern Demo")
    print("=" * 80)
    
    # Create hybrid pattern
    pattern = SymbolicNeuralHybridPattern()
    hybrid = pattern.hybrid
    
    # Add symbolic rules
    hybrid.symbolic.add_rule(Rule(
        rule_id="rule-1",
        name="Mammal Classification",
        conditions=["has_fur", "warm_blooded", "feeds_milk"],
        conclusion="is_mammal",
        confidence=0.95
    ))
    
    hybrid.symbolic.add_rule(Rule(
        rule_id="rule-2",
        name="Bird Classification",
        conditions=["has_feathers", "has_wings", "lays_eggs"],
        conclusion="is_bird",
        confidence=0.95
    ))
    
    hybrid.symbolic.add_rule(Rule(
        rule_id="rule-3",
        name="Carnivore Classification",
        conditions=["eats_meat", "has_sharp_teeth"],
        conclusion="is_carnivore",
        confidence=0.9
    ))
    
    # Add facts
    hybrid.symbolic.add_fact(Fact(
        fact_id="f1",
        predicate="has_fur",
        arguments=["dog"],
        confidence=1.0
    ))
    
    hybrid.symbolic.add_fact(Fact(
        fact_id="f2",
        predicate="warm_blooded",
        arguments=["dog"],
        confidence=1.0
    ))
    
    hybrid.symbolic.add_fact(Fact(
        fact_id="f3",
        predicate="feeds_milk",
        arguments=["dog"],
        confidence=1.0
    ))
    
    hybrid.symbolic.add_fact(Fact(
        fact_id="f4",
        predicate="eats_meat",
        arguments=["dog"],
        confidence=0.8
    ))
    
    # Test different reasoning modes
    queries = [
        "What type of animal is a dog?",
        "Is a dog a carnivore?",
        "What can we infer about a dog's diet?"
    ]
    
    modes = [ReasoningMode.SYMBOLIC, ReasoningMode.NEURAL, ReasoningMode.HYBRID]
    
    for query in queries:
        print(f"\n{'=' * 80}")
        print(f"🔍 Query: {query}")
        print('=' * 80)
        
        for mode in modes:
            print(f"\n📊 Mode: {mode.value.upper()}")
            print('-' * 80)
            
            result = pattern.run(query, mode)
            
            for i, res in enumerate(result["results"], 1):
                print(f"\n  Result {i}:")
                print(f"    Conclusion: {res['conclusion']}")
                print(f"    Confidence: {res['confidence']:.2f}")
                print(f"    Mode: {res['mode']}")
                print(f"    Explanation: {res['explanation']}")
                if res['supporting_facts_count'] > 0:
                    print(f"    Supporting Facts: {res['supporting_facts_count']}")
                if res['applied_rules_count'] > 0:
                    print(f"    Applied Rules: {res['applied_rules_count']}")


if __name__ == "__main__":
    main()

Agentic Architecture Pattern: Symbolic Neural Hybrid

Pattern 151: 151 Symbolic Neural Hybrid Pattern – Line-by-Line Explanation

Overview

Pattern 151: Symbolic Neural Hybrid Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 151
Pattern Name: 151 Symbolic Neural Hybrid Pattern
File Path: patterns\hybrid_ai\151_symbolic_neural_hybrid_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 151: Symbolic Neural Hybrid Pattern

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

Explanation: This module docstring describes the purpose and key features of the 151 Symbolic Neural Hybrid Pattern pattern.

Imports Section

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

Classes

SymbolicNeuralHybridConfig (Line 18)

Description: Configuration for Symbolic Neural Hybrid Pattern.

SymbolicNeuralHybridState (Line 27)

Description: State information for Symbolic Neural Hybrid Pattern.

SymbolicNeuralHybridAgent (Line 36)

Description: Agent implementing Symbolic Neural Hybrid Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

def process(self, input_data):
    """Process input according to Symbolic Neural Hybrid Pattern."""

Purpose: Process input according to Symbolic Neural Hybrid Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

SymbolicNeuralHybridPattern (Line 85)

Description: Symbolic Neural Hybrid Pattern – Enhanced implementation

This pattern provides comprehensive functionality for symbolic neural hybrid 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 = SymbolicNeuralHybridConfig(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: Subtask Delegation


    """
Pattern 150: Subtask Delegation Pattern

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

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


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


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


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


class SubtaskDelegationPattern:
    """
    Subtask Delegation Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for subtask delegation
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: SubtaskDelegationConfig = None):
        self.config = config or SubtaskDelegationConfig()
        self.agents: Dict[str, SubtaskDelegationAgent] = {}
        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 Subtask Delegation Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: SubtaskDelegationAgent):
        """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": "Subtask Delegation Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Subtask Delegation Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Subtask Delegation Pattern Demo ===\n")
    
    # Create configuration
    config = SubtaskDelegationConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = SubtaskDelegationPattern(config)
    
    # Create and register agents
    agent1 = SubtaskDelegationAgent("Agent1", config)
    agent2 = SubtaskDelegationAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate subtask delegation 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: Subtask Delegation

Pattern 150: 150 Subtask Delegation Pattern – Line-by-Line Explanation

Overview

Pattern 150: Subtask Delegation Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 150
Pattern Name: 150 Subtask Delegation Pattern
File Path: patterns\workflow\150_subtask_delegation_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 150: Subtask Delegation Pattern

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

Explanation: This module docstring describes the purpose and key features of the 150 Subtask Delegation Pattern pattern.

Imports Section

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

Classes

SubtaskDelegationConfig (Line 18)

Description: Configuration for Subtask Delegation Pattern.

SubtaskDelegationState (Line 27)

Description: State information for Subtask Delegation Pattern.

SubtaskDelegationAgent (Line 36)

Description: Agent implementing Subtask Delegation Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

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

Purpose: Process input according to Subtask Delegation Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

SubtaskDelegationPattern (Line 85)

Description: Subtask Delegation Pattern – Enhanced implementation

This pattern provides comprehensive functionality for subtask delegation 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 = SubtaskDelegationConfig(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: chatgpt

Agentic Architecture Pattern: Causality Based

Agentic Architecture Pattern: Causality Based

Pattern 159: 159 Causality Based Pattern – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Classes

CausalityBasedConfig (Line 18)

CausalityBasedState (Line 27)

CausalityBasedAgent (Line 36)

__init__ (Line 39)

process (Line 49)

get_statistics (Line 75)

CausalityBasedPattern (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: Interpretable Agent

Agentic Architecture Pattern: Interpretable Agent

Pattern 158: 158 Interpretable Agent Pattern – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Classes

InterpretableAgentConfig (Line 18)

InterpretableAgentState (Line 27)

InterpretableAgentAgent (Line 36)

__init__ (Line 39)

process (Line 49)

get_statistics (Line 75)

InterpretableAgentPattern (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: Transparent Agent

Agentic Architecture Pattern: Transparent Agent

Pattern 157: 157 Transparent Agent Pattern – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Classes

TransparentAgentConfig (Line 18)

TransparentAgentState (Line 27)

TransparentAgentAgent (Line 36)

__init__ (Line 39)

process (Line 49)

get_statistics (Line 75)

TransparentAgentPattern (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 44)

`create_task` (Line 49)

`agent_process` (Line 55)

`incorporate_human_input` (Line 77)

`incorporate_human_feedback` (Line 85)

`complete_task` (Line 104)

`run` (Line 117)

`get_stats` (Line 144)