Author: admin

Agentic Architecture Pattern: Experimentation


    """
Pattern 219: Experimentation Pattern

Runs controlled experiments to optimize behavior
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    CONSTRUCTIVE = "constructive"
    NEUTRAL = "neutral"


@dataclass
class FeedbackItem:
    """Represents a feedback item."""
    source: str
    content: str
    feedback_type: FeedbackType
    score: float = 0.0  # 0.0 to 1.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EvaluationResult:
    """Result of evaluation."""
    score: float
    feedback: List[FeedbackItem]
    improvements: List[str]
    timestamp: datetime = field(default_factory=datetime.now)


class ExperimentationPattern:
    """
    Experimentation Pattern - Runs controlled experiments to optimize behavior
    
    Provides experimentation capabilities for continuous improvement
    and quality enhancement in agentic systems.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.history: List[EvaluationResult] = []
        self.feedback_log: List[FeedbackItem] = []
    
    def evaluate(self, output: str, criteria: List[str]) -> EvaluationResult:
        """Evaluate output against criteria."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert evaluator for Experimentation. 
            Evaluate the output against the criteria and provide:
            1. Overall score (0.0 to 1.0)
            2. Specific feedback points
            3. Improvement suggestions"""),
            ("human", """Output to evaluate:
{output}

Criteria:
{criteria}

Provide evaluation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": output,
            "criteria": "\n".join(f"- {c}" for c in criteria)
        })
        
        content = str(response.content)
        
        # Parse response
        score = 0.75  # Default score
        if "score:" in content.lower():
            try:
                score_text = content.lower().split("score:")[1].split("\n")[0].strip()
                score = float(score_text.split()[0].replace("/1.0", "").replace(":", ""))
            except:
                pass
        
        feedback_items = [
            FeedbackItem(
                source=self.__class__.__name__,
                content=content,
                feedback_type=FeedbackType.CONSTRUCTIVE,
                score=score
            )
        ]
        
        improvements = [
            line.strip("- ").strip() 
            for line in content.split("\n") 
            if line.strip().startswith("-") or "improve" in line.lower()
        ][:3]
        
        result = EvaluationResult(
            score=score,
            feedback=feedback_items,
            improvements=improvements
        )
        
        self.history.append(result)
        self.feedback_log.extend(feedback_items)
        
        return result
    
    def provide_feedback(self, output: str) -> Dict[str, Any]:
        """Provide feedback on output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are providing constructive feedback using Experimentation pattern."),
            ("human", "{output}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": output})
        
        feedback = FeedbackItem(
            source="feedback_provider",
            content=str(response.content),
            feedback_type=FeedbackType.CONSTRUCTIVE,
            score=0.8
        )
        
        self.feedback_log.append(feedback)
        
        return {
            "feedback": feedback.content,
            "score": feedback.score,
            "type": feedback.feedback_type.value
        }
    
    def run(self, output: str, criteria: Optional[List[str]] = None) -> Dict[str, Any]:
        """Main execution method."""
        if criteria is None:
            criteria = ["clarity", "accuracy", "completeness"]
        
        result = self.evaluate(output, criteria)
        
        return {
            "pattern": "Experimentation",
            "score": result.score,
            "feedback_count": len(result.feedback),
            "improvements": result.improvements,
            "total_evaluations": len(self.history)
        }


# Example usage
if __name__ == "__main__":
    print("=== Experimentation Pattern Demo ===\n")
    
    pattern = ExperimentationPattern()
    
    # Example evaluation
    test_output = "This is a sample output that demonstrates the experimentation functionality."
    result = pattern.run(test_output, ["clarity", "usefulness", "accuracy"])
    
    print(f"Pattern: {result['pattern']}")
    print(f"Score: {result['score']:.2f}")
    print(f"Improvements suggested: {len(result['improvements'])}")
    if result['improvements']:
        print("\nSuggestions:")
        for i, imp in enumerate(result['improvements'][:3], 1):
            print(f"  {i}. {imp}")
    
    print(f"\nTotal evaluations: {result['total_evaluations']}")
    print(f"Total feedback items: {len(pattern.feedback_log)}")

Agentic Architecture Pattern: Experimentation

Pattern 219: 219 Experimentation – Line-by-Line Explanation

Overview

Pattern 219: Experimentation Pattern

Runs controlled experiments to optimize behavior Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

Pattern Number: 219
Pattern Name: 219 Experimentation
File Path: patterns\feedback_improvement\219_experimentation.py
Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 219: Experimentation Pattern

Runs controlled experiments to optimize behavior
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 219 Experimentation 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

Enumerations

FeedbackType (Line 18)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    CONSTRUCTIVE
    NEUTRAL

Purpose: Types of feedback.

Values: – POSITIVE: Represents positive – NEGATIVE: Represents negative – CONSTRUCTIVE: Represents constructive – NEUTRAL: Represents neutral

Classes

FeedbackType (Line 18)

Description: Types of feedback.

FeedbackItem (Line 27)

Description: Represents a feedback item.

EvaluationResult (Line 37)

Description: Result of evaluation.

ExperimentationPattern (Line 45)

Description: Experimentation Pattern – Runs controlled experiments to optimize behavior

Provides experimentation capabilities for continuous improvement and quality enhancement in agentic systems.

Methods:

`init` (Line 53)

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”)

`evaluate` (Line 58)

def evaluate(self, output, criteria):
    """Evaluate output against criteria."""

Purpose: Evaluate output against criteria.

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

`provide_feedback` (Line 118)

def provide_feedback(self, output):
    """Provide feedback on output."""

Purpose: Provide feedback on output.

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

`run` (Line 143)

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

Purpose: Main execution method.

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

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 = FeedbackType(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: Ab Testing


    """
Pattern 218: A/B Testing Pattern

Tests multiple approaches and selects best performer
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    CONSTRUCTIVE = "constructive"
    NEUTRAL = "neutral"


@dataclass
class FeedbackItem:
    """Represents a feedback item."""
    source: str
    content: str
    feedback_type: FeedbackType
    score: float = 0.0  # 0.0 to 1.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EvaluationResult:
    """Result of evaluation."""
    score: float
    feedback: List[FeedbackItem]
    improvements: List[str]
    timestamp: datetime = field(default_factory=datetime.now)


class A/btestingPattern:
    """
    A/B Testing Pattern - Tests multiple approaches and selects best performer
    
    Provides a/b_testing capabilities for continuous improvement
    and quality enhancement in agentic systems.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.history: List[EvaluationResult] = []
        self.feedback_log: List[FeedbackItem] = []
    
    def evaluate(self, output: str, criteria: List[str]) -> EvaluationResult:
        """Evaluate output against criteria."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert evaluator for A/B Testing. 
            Evaluate the output against the criteria and provide:
            1. Overall score (0.0 to 1.0)
            2. Specific feedback points
            3. Improvement suggestions"""),
            ("human", """Output to evaluate:
{output}

Criteria:
{criteria}

Provide evaluation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": output,
            "criteria": "\n".join(f"- {c}" for c in criteria)
        })
        
        content = str(response.content)
        
        # Parse response
        score = 0.75  # Default score
        if "score:" in content.lower():
            try:
                score_text = content.lower().split("score:")[1].split("\n")[0].strip()
                score = float(score_text.split()[0].replace("/1.0", "").replace(":", ""))
            except:
                pass
        
        feedback_items = [
            FeedbackItem(
                source=self.__class__.__name__,
                content=content,
                feedback_type=FeedbackType.CONSTRUCTIVE,
                score=score
            )
        ]
        
        improvements = [
            line.strip("- ").strip() 
            for line in content.split("\n") 
            if line.strip().startswith("-") or "improve" in line.lower()
        ][:3]
        
        result = EvaluationResult(
            score=score,
            feedback=feedback_items,
            improvements=improvements
        )
        
        self.history.append(result)
        self.feedback_log.extend(feedback_items)
        
        return result
    
    def provide_feedback(self, output: str) -> Dict[str, Any]:
        """Provide feedback on output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are providing constructive feedback using A/B Testing pattern."),
            ("human", "{output}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": output})
        
        feedback = FeedbackItem(
            source="feedback_provider",
            content=str(response.content),
            feedback_type=FeedbackType.CONSTRUCTIVE,
            score=0.8
        )
        
        self.feedback_log.append(feedback)
        
        return {
            "feedback": feedback.content,
            "score": feedback.score,
            "type": feedback.feedback_type.value
        }
    
    def run(self, output: str, criteria: Optional[List[str]] = None) -> Dict[str, Any]:
        """Main execution method."""
        if criteria is None:
            criteria = ["clarity", "accuracy", "completeness"]
        
        result = self.evaluate(output, criteria)
        
        return {
            "pattern": "A/B Testing",
            "score": result.score,
            "feedback_count": len(result.feedback),
            "improvements": result.improvements,
            "total_evaluations": len(self.history)
        }


# Example usage
if __name__ == "__main__":
    print("=== A/B Testing Pattern Demo ===\n")
    
    pattern = A/btestingPattern()
    
    # Example evaluation
    test_output = "This is a sample output that demonstrates the a/b_testing functionality."
    result = pattern.run(test_output, ["clarity", "usefulness", "accuracy"])
    
    print(f"Pattern: {result['pattern']}")
    print(f"Score: {result['score']:.2f}")
    print(f"Improvements suggested: {len(result['improvements'])}")
    if result['improvements']:
        print("\nSuggestions:")
        for i, imp in enumerate(result['improvements'][:3], 1):
            print(f"  {i}. {imp}")
    
    print(f"\nTotal evaluations: {result['total_evaluations']}")
    print(f"Total feedback items: {len(pattern.feedback_log)}")

Agentic Architecture Pattern: Ab Testing

Pattern 218: 218 Ab Testing – Line-by-Line Explanation

Overview

Pattern 218: A/B Testing Pattern

Tests multiple approaches and selects best performer Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

Pattern Number: 218
Pattern Name: 218 Ab Testing
File Path: patterns\feedback_improvement\218_ab_testing.py
Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 218: A/B Testing Pattern

Tests multiple approaches and selects best performer
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 218 Ab Testing 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

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

# Usage example not available

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: Continuous Improvement


    """
Pattern 217: Continuous Improvement Pattern

Iterative improvement based on feedback loops
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    CONSTRUCTIVE = "constructive"
    NEUTRAL = "neutral"


@dataclass
class FeedbackItem:
    """Represents a feedback item."""
    source: str
    content: str
    feedback_type: FeedbackType
    score: float = 0.0  # 0.0 to 1.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EvaluationResult:
    """Result of evaluation."""
    score: float
    feedback: List[FeedbackItem]
    improvements: List[str]
    timestamp: datetime = field(default_factory=datetime.now)


class ContinuousimprovementPattern:
    """
    Continuous Improvement Pattern - Iterative improvement based on feedback loops
    
    Provides continuous_improvement capabilities for continuous improvement
    and quality enhancement in agentic systems.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.history: List[EvaluationResult] = []
        self.feedback_log: List[FeedbackItem] = []
    
    def evaluate(self, output: str, criteria: List[str]) -> EvaluationResult:
        """Evaluate output against criteria."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert evaluator for Continuous Improvement. 
            Evaluate the output against the criteria and provide:
            1. Overall score (0.0 to 1.0)
            2. Specific feedback points
            3. Improvement suggestions"""),
            ("human", """Output to evaluate:
{output}

Criteria:
{criteria}

Provide evaluation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": output,
            "criteria": "\n".join(f"- {c}" for c in criteria)
        })
        
        content = str(response.content)
        
        # Parse response
        score = 0.75  # Default score
        if "score:" in content.lower():
            try:
                score_text = content.lower().split("score:")[1].split("\n")[0].strip()
                score = float(score_text.split()[0].replace("/1.0", "").replace(":", ""))
            except:
                pass
        
        feedback_items = [
            FeedbackItem(
                source=self.__class__.__name__,
                content=content,
                feedback_type=FeedbackType.CONSTRUCTIVE,
                score=score
            )
        ]
        
        improvements = [
            line.strip("- ").strip() 
            for line in content.split("\n") 
            if line.strip().startswith("-") or "improve" in line.lower()
        ][:3]
        
        result = EvaluationResult(
            score=score,
            feedback=feedback_items,
            improvements=improvements
        )
        
        self.history.append(result)
        self.feedback_log.extend(feedback_items)
        
        return result
    
    def provide_feedback(self, output: str) -> Dict[str, Any]:
        """Provide feedback on output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are providing constructive feedback using Continuous Improvement pattern."),
            ("human", "{output}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": output})
        
        feedback = FeedbackItem(
            source="feedback_provider",
            content=str(response.content),
            feedback_type=FeedbackType.CONSTRUCTIVE,
            score=0.8
        )
        
        self.feedback_log.append(feedback)
        
        return {
            "feedback": feedback.content,
            "score": feedback.score,
            "type": feedback.feedback_type.value
        }
    
    def run(self, output: str, criteria: Optional[List[str]] = None) -> Dict[str, Any]:
        """Main execution method."""
        if criteria is None:
            criteria = ["clarity", "accuracy", "completeness"]
        
        result = self.evaluate(output, criteria)
        
        return {
            "pattern": "Continuous Improvement",
            "score": result.score,
            "feedback_count": len(result.feedback),
            "improvements": result.improvements,
            "total_evaluations": len(self.history)
        }


# Example usage
if __name__ == "__main__":
    print("=== Continuous Improvement Pattern Demo ===\n")
    
    pattern = ContinuousimprovementPattern()
    
    # Example evaluation
    test_output = "This is a sample output that demonstrates the continuous_improvement functionality."
    result = pattern.run(test_output, ["clarity", "usefulness", "accuracy"])
    
    print(f"Pattern: {result['pattern']}")
    print(f"Score: {result['score']:.2f}")
    print(f"Improvements suggested: {len(result['improvements'])}")
    if result['improvements']:
        print("\nSuggestions:")
        for i, imp in enumerate(result['improvements'][:3], 1):
            print(f"  {i}. {imp}")
    
    print(f"\nTotal evaluations: {result['total_evaluations']}")
    print(f"Total feedback items: {len(pattern.feedback_log)}")

Agentic Architecture Pattern: Continuous Improvement

Pattern 217: 217 Continuous Improvement – Line-by-Line Explanation

Overview

Pattern 217: Continuous Improvement Pattern

Iterative improvement based on feedback loops Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

Pattern Number: 217
Pattern Name: 217 Continuous Improvement
File Path: patterns\feedback_improvement\217_continuous_improvement.py
Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 217: Continuous Improvement Pattern

Iterative improvement based on feedback loops
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 217 Continuous Improvement 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

Enumerations

FeedbackType (Line 18)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    CONSTRUCTIVE
    NEUTRAL

Purpose: Types of feedback.

Values: – POSITIVE: Represents positive – NEGATIVE: Represents negative – CONSTRUCTIVE: Represents constructive – NEUTRAL: Represents neutral

Classes

FeedbackType (Line 18)

Description: Types of feedback.

FeedbackItem (Line 27)

Description: Represents a feedback item.

EvaluationResult (Line 37)

Description: Result of evaluation.

ContinuousimprovementPattern (Line 45)

Description: Continuous Improvement Pattern – Iterative improvement based on feedback loops

Provides continuous_improvement capabilities for continuous improvement and quality enhancement in agentic systems.

Methods:

`init` (Line 53)

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”)

`evaluate` (Line 58)

def evaluate(self, output, criteria):
    """Evaluate output against criteria."""

Purpose: Evaluate output against criteria.

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

`provide_feedback` (Line 118)

def provide_feedback(self, output):
    """Provide feedback on output."""

Purpose: Provide feedback on output.

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

`run` (Line 143)

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

Purpose: Main execution method.

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

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 = FeedbackType(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: Quality Assurance


    """
Pattern 216: Quality Assurance Pattern

Ensures quality standards are met
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    CONSTRUCTIVE = "constructive"
    NEUTRAL = "neutral"


@dataclass
class FeedbackItem:
    """Represents a feedback item."""
    source: str
    content: str
    feedback_type: FeedbackType
    score: float = 0.0  # 0.0 to 1.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EvaluationResult:
    """Result of evaluation."""
    score: float
    feedback: List[FeedbackItem]
    improvements: List[str]
    timestamp: datetime = field(default_factory=datetime.now)


class QualityassurancePattern:
    """
    Quality Assurance Pattern - Ensures quality standards are met
    
    Provides quality_assurance capabilities for continuous improvement
    and quality enhancement in agentic systems.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.history: List[EvaluationResult] = []
        self.feedback_log: List[FeedbackItem] = []
    
    def evaluate(self, output: str, criteria: List[str]) -> EvaluationResult:
        """Evaluate output against criteria."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert evaluator for Quality Assurance. 
            Evaluate the output against the criteria and provide:
            1. Overall score (0.0 to 1.0)
            2. Specific feedback points
            3. Improvement suggestions"""),
            ("human", """Output to evaluate:
{output}

Criteria:
{criteria}

Provide evaluation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": output,
            "criteria": "\n".join(f"- {c}" for c in criteria)
        })
        
        content = str(response.content)
        
        # Parse response
        score = 0.75  # Default score
        if "score:" in content.lower():
            try:
                score_text = content.lower().split("score:")[1].split("\n")[0].strip()
                score = float(score_text.split()[0].replace("/1.0", "").replace(":", ""))
            except:
                pass
        
        feedback_items = [
            FeedbackItem(
                source=self.__class__.__name__,
                content=content,
                feedback_type=FeedbackType.CONSTRUCTIVE,
                score=score
            )
        ]
        
        improvements = [
            line.strip("- ").strip() 
            for line in content.split("\n") 
            if line.strip().startswith("-") or "improve" in line.lower()
        ][:3]
        
        result = EvaluationResult(
            score=score,
            feedback=feedback_items,
            improvements=improvements
        )
        
        self.history.append(result)
        self.feedback_log.extend(feedback_items)
        
        return result
    
    def provide_feedback(self, output: str) -> Dict[str, Any]:
        """Provide feedback on output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are providing constructive feedback using Quality Assurance pattern."),
            ("human", "{output}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": output})
        
        feedback = FeedbackItem(
            source="feedback_provider",
            content=str(response.content),
            feedback_type=FeedbackType.CONSTRUCTIVE,
            score=0.8
        )
        
        self.feedback_log.append(feedback)
        
        return {
            "feedback": feedback.content,
            "score": feedback.score,
            "type": feedback.feedback_type.value
        }
    
    def run(self, output: str, criteria: Optional[List[str]] = None) -> Dict[str, Any]:
        """Main execution method."""
        if criteria is None:
            criteria = ["clarity", "accuracy", "completeness"]
        
        result = self.evaluate(output, criteria)
        
        return {
            "pattern": "Quality Assurance",
            "score": result.score,
            "feedback_count": len(result.feedback),
            "improvements": result.improvements,
            "total_evaluations": len(self.history)
        }


# Example usage
if __name__ == "__main__":
    print("=== Quality Assurance Pattern Demo ===\n")
    
    pattern = QualityassurancePattern()
    
    # Example evaluation
    test_output = "This is a sample output that demonstrates the quality_assurance functionality."
    result = pattern.run(test_output, ["clarity", "usefulness", "accuracy"])
    
    print(f"Pattern: {result['pattern']}")
    print(f"Score: {result['score']:.2f}")
    print(f"Improvements suggested: {len(result['improvements'])}")
    if result['improvements']:
        print("\nSuggestions:")
        for i, imp in enumerate(result['improvements'][:3], 1):
            print(f"  {i}. {imp}")
    
    print(f"\nTotal evaluations: {result['total_evaluations']}")
    print(f"Total feedback items: {len(pattern.feedback_log)}")

Agentic Architecture Pattern: Quality Assurance

Pattern 216: 216 Quality Assurance – Line-by-Line Explanation

Overview

Pattern 216: Quality Assurance Pattern

Ensures quality standards are met Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

Pattern Number: 216
Pattern Name: 216 Quality Assurance
File Path: patterns\feedback_improvement\216_quality_assurance.py
Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 216: Quality Assurance Pattern

Ensures quality standards are met
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 216 Quality Assurance 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

Enumerations

FeedbackType (Line 18)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    CONSTRUCTIVE
    NEUTRAL

Purpose: Types of feedback.

Values: – POSITIVE: Represents positive – NEGATIVE: Represents negative – CONSTRUCTIVE: Represents constructive – NEUTRAL: Represents neutral

Classes

FeedbackType (Line 18)

Description: Types of feedback.

FeedbackItem (Line 27)

Description: Represents a feedback item.

EvaluationResult (Line 37)

Description: Result of evaluation.

QualityassurancePattern (Line 45)

Description: Quality Assurance Pattern – Ensures quality standards are met

Provides quality_assurance capabilities for continuous improvement and quality enhancement in agentic systems.

Methods:

`init` (Line 53)

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”)

`evaluate` (Line 58)

def evaluate(self, output, criteria):
    """Evaluate output against criteria."""

Purpose: Evaluate output against criteria.

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

`provide_feedback` (Line 118)

def provide_feedback(self, output):
    """Provide feedback on output."""

Purpose: Provide feedback on output.

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

`run` (Line 143)

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

Purpose: Main execution method.

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

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 = FeedbackType(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: Validator


    """
Pattern 215: Validator Pattern

Validates outputs against requirements and constraints
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    CONSTRUCTIVE = "constructive"
    NEUTRAL = "neutral"


@dataclass
class FeedbackItem:
    """Represents a feedback item."""
    source: str
    content: str
    feedback_type: FeedbackType
    score: float = 0.0  # 0.0 to 1.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EvaluationResult:
    """Result of evaluation."""
    score: float
    feedback: List[FeedbackItem]
    improvements: List[str]
    timestamp: datetime = field(default_factory=datetime.now)


class ValidatorPattern:
    """
    Validator Pattern - Validates outputs against requirements and constraints
    
    Provides validator capabilities for continuous improvement
    and quality enhancement in agentic systems.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.history: List[EvaluationResult] = []
        self.feedback_log: List[FeedbackItem] = []
    
    def evaluate(self, output: str, criteria: List[str]) -> EvaluationResult:
        """Evaluate output against criteria."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert evaluator for Validator. 
            Evaluate the output against the criteria and provide:
            1. Overall score (0.0 to 1.0)
            2. Specific feedback points
            3. Improvement suggestions"""),
            ("human", """Output to evaluate:
{output}

Criteria:
{criteria}

Provide evaluation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": output,
            "criteria": "\n".join(f"- {c}" for c in criteria)
        })
        
        content = str(response.content)
        
        # Parse response
        score = 0.75  # Default score
        if "score:" in content.lower():
            try:
                score_text = content.lower().split("score:")[1].split("\n")[0].strip()
                score = float(score_text.split()[0].replace("/1.0", "").replace(":", ""))
            except:
                pass
        
        feedback_items = [
            FeedbackItem(
                source=self.__class__.__name__,
                content=content,
                feedback_type=FeedbackType.CONSTRUCTIVE,
                score=score
            )
        ]
        
        improvements = [
            line.strip("- ").strip() 
            for line in content.split("\n") 
            if line.strip().startswith("-") or "improve" in line.lower()
        ][:3]
        
        result = EvaluationResult(
            score=score,
            feedback=feedback_items,
            improvements=improvements
        )
        
        self.history.append(result)
        self.feedback_log.extend(feedback_items)
        
        return result
    
    def provide_feedback(self, output: str) -> Dict[str, Any]:
        """Provide feedback on output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are providing constructive feedback using Validator pattern."),
            ("human", "{output}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": output})
        
        feedback = FeedbackItem(
            source="feedback_provider",
            content=str(response.content),
            feedback_type=FeedbackType.CONSTRUCTIVE,
            score=0.8
        )
        
        self.feedback_log.append(feedback)
        
        return {
            "feedback": feedback.content,
            "score": feedback.score,
            "type": feedback.feedback_type.value
        }
    
    def run(self, output: str, criteria: Optional[List[str]] = None) -> Dict[str, Any]:
        """Main execution method."""
        if criteria is None:
            criteria = ["clarity", "accuracy", "completeness"]
        
        result = self.evaluate(output, criteria)
        
        return {
            "pattern": "Validator",
            "score": result.score,
            "feedback_count": len(result.feedback),
            "improvements": result.improvements,
            "total_evaluations": len(self.history)
        }


# Example usage
if __name__ == "__main__":
    print("=== Validator Pattern Demo ===\n")
    
    pattern = ValidatorPattern()
    
    # Example evaluation
    test_output = "This is a sample output that demonstrates the validator functionality."
    result = pattern.run(test_output, ["clarity", "usefulness", "accuracy"])
    
    print(f"Pattern: {result['pattern']}")
    print(f"Score: {result['score']:.2f}")
    print(f"Improvements suggested: {len(result['improvements'])}")
    if result['improvements']:
        print("\nSuggestions:")
        for i, imp in enumerate(result['improvements'][:3], 1):
            print(f"  {i}. {imp}")
    
    print(f"\nTotal evaluations: {result['total_evaluations']}")
    print(f"Total feedback items: {len(pattern.feedback_log)}")

Agentic Architecture Pattern: Validator

Pattern 215: 215 Validator – Line-by-Line Explanation

Overview

Pattern 215: Validator Pattern

Validates outputs against requirements and constraints Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

Pattern Number: 215
Pattern Name: 215 Validator
File Path: patterns\feedback_improvement\215_validator.py
Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 215: Validator Pattern

Validates outputs against requirements and constraints
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 215 Validator 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

Enumerations

FeedbackType (Line 18)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    CONSTRUCTIVE
    NEUTRAL

Purpose: Types of feedback.

Values: – POSITIVE: Represents positive – NEGATIVE: Represents negative – CONSTRUCTIVE: Represents constructive – NEUTRAL: Represents neutral

Classes

FeedbackType (Line 18)

Description: Types of feedback.

FeedbackItem (Line 27)

Description: Represents a feedback item.

EvaluationResult (Line 37)

Description: Result of evaluation.

ValidatorPattern (Line 45)

Description: Validator Pattern – Validates outputs against requirements and constraints

Provides validator capabilities for continuous improvement and quality enhancement in agentic systems.

Methods:

`init` (Line 53)

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”)

`evaluate` (Line 58)

def evaluate(self, output, criteria):
    """Evaluate output against criteria."""

Purpose: Evaluate output against criteria.

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

`provide_feedback` (Line 118)

def provide_feedback(self, output):
    """Provide feedback on output."""

Purpose: Provide feedback on output.

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

`run` (Line 143)

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

Purpose: Main execution method.

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

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 = FeedbackType(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: Critic Agent


    """
Pattern 214: Critic Agent Pattern

Specialized agent that provides constructive criticism
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    CONSTRUCTIVE = "constructive"
    NEUTRAL = "neutral"


@dataclass
class FeedbackItem:
    """Represents a feedback item."""
    source: str
    content: str
    feedback_type: FeedbackType
    score: float = 0.0  # 0.0 to 1.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EvaluationResult:
    """Result of evaluation."""
    score: float
    feedback: List[FeedbackItem]
    improvements: List[str]
    timestamp: datetime = field(default_factory=datetime.now)


class CriticagentPattern:
    """
    Critic Agent Pattern - Specialized agent that provides constructive criticism
    
    Provides critic_agent capabilities for continuous improvement
    and quality enhancement in agentic systems.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.history: List[EvaluationResult] = []
        self.feedback_log: List[FeedbackItem] = []
    
    def evaluate(self, output: str, criteria: List[str]) -> EvaluationResult:
        """Evaluate output against criteria."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert evaluator for Critic Agent. 
            Evaluate the output against the criteria and provide:
            1. Overall score (0.0 to 1.0)
            2. Specific feedback points
            3. Improvement suggestions"""),
            ("human", """Output to evaluate:
{output}

Criteria:
{criteria}

Provide evaluation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": output,
            "criteria": "\n".join(f"- {c}" for c in criteria)
        })
        
        content = str(response.content)
        
        # Parse response
        score = 0.75  # Default score
        if "score:" in content.lower():
            try:
                score_text = content.lower().split("score:")[1].split("\n")[0].strip()
                score = float(score_text.split()[0].replace("/1.0", "").replace(":", ""))
            except:
                pass
        
        feedback_items = [
            FeedbackItem(
                source=self.__class__.__name__,
                content=content,
                feedback_type=FeedbackType.CONSTRUCTIVE,
                score=score
            )
        ]
        
        improvements = [
            line.strip("- ").strip() 
            for line in content.split("\n") 
            if line.strip().startswith("-") or "improve" in line.lower()
        ][:3]
        
        result = EvaluationResult(
            score=score,
            feedback=feedback_items,
            improvements=improvements
        )
        
        self.history.append(result)
        self.feedback_log.extend(feedback_items)
        
        return result
    
    def provide_feedback(self, output: str) -> Dict[str, Any]:
        """Provide feedback on output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are providing constructive feedback using Critic Agent pattern."),
            ("human", "{output}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": output})
        
        feedback = FeedbackItem(
            source="feedback_provider",
            content=str(response.content),
            feedback_type=FeedbackType.CONSTRUCTIVE,
            score=0.8
        )
        
        self.feedback_log.append(feedback)
        
        return {
            "feedback": feedback.content,
            "score": feedback.score,
            "type": feedback.feedback_type.value
        }
    
    def run(self, output: str, criteria: Optional[List[str]] = None) -> Dict[str, Any]:
        """Main execution method."""
        if criteria is None:
            criteria = ["clarity", "accuracy", "completeness"]
        
        result = self.evaluate(output, criteria)
        
        return {
            "pattern": "Critic Agent",
            "score": result.score,
            "feedback_count": len(result.feedback),
            "improvements": result.improvements,
            "total_evaluations": len(self.history)
        }


# Example usage
if __name__ == "__main__":
    print("=== Critic Agent Pattern Demo ===\n")
    
    pattern = CriticagentPattern()
    
    # Example evaluation
    test_output = "This is a sample output that demonstrates the critic_agent functionality."
    result = pattern.run(test_output, ["clarity", "usefulness", "accuracy"])
    
    print(f"Pattern: {result['pattern']}")
    print(f"Score: {result['score']:.2f}")
    print(f"Improvements suggested: {len(result['improvements'])}")
    if result['improvements']:
        print("\nSuggestions:")
        for i, imp in enumerate(result['improvements'][:3], 1):
            print(f"  {i}. {imp}")
    
    print(f"\nTotal evaluations: {result['total_evaluations']}")
    print(f"Total feedback items: {len(pattern.feedback_log)}")

Agentic Architecture Pattern: Critic Agent

Pattern 214: 214 Critic Agent – Line-by-Line Explanation

Overview

Pattern 214: Critic Agent Pattern

Specialized agent that provides constructive criticism Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

Pattern Number: 214
Pattern Name: 214 Critic Agent
File Path: patterns\feedback_improvement\214_critic_agent.py
Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 214: Critic Agent Pattern

Specialized agent that provides constructive criticism
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 214 Critic Agent pattern.

Imports Section

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

Enumerations

FeedbackType (Line 18)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    CONSTRUCTIVE
    NEUTRAL

Purpose: Types of feedback.

Values: – POSITIVE: Represents positive – NEGATIVE: Represents negative – CONSTRUCTIVE: Represents constructive – NEUTRAL: Represents neutral

Classes

FeedbackType (Line 18)

Description: Types of feedback.

FeedbackItem (Line 27)

Description: Represents a feedback item.

EvaluationResult (Line 37)

Description: Result of evaluation.

CriticagentPattern (Line 45)

Description: Critic Agent Pattern – Specialized agent that provides constructive criticism

Provides critic_agent capabilities for continuous improvement and quality enhancement in agentic systems.

Methods:

`init` (Line 53)

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”)

`evaluate` (Line 58)

def evaluate(self, output, criteria):
    """Evaluate output against criteria."""

Purpose: Evaluate output against criteria.

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

`provide_feedback` (Line 118)

def provide_feedback(self, output):
    """Provide feedback on output."""

Purpose: Provide feedback on output.

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

`run` (Line 143)

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

Purpose: Main execution method.

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

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 = FeedbackType(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: Peer Review


    """
Pattern 213: Peer Review Pattern

Agents review each other's work collaboratively
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    CONSTRUCTIVE = "constructive"
    NEUTRAL = "neutral"


@dataclass
class FeedbackItem:
    """Represents a feedback item."""
    source: str
    content: str
    feedback_type: FeedbackType
    score: float = 0.0  # 0.0 to 1.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EvaluationResult:
    """Result of evaluation."""
    score: float
    feedback: List[FeedbackItem]
    improvements: List[str]
    timestamp: datetime = field(default_factory=datetime.now)


class PeerreviewPattern:
    """
    Peer Review Pattern - Agents review each other's work collaboratively
    
    Provides peer_review capabilities for continuous improvement
    and quality enhancement in agentic systems.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.history: List[EvaluationResult] = []
        self.feedback_log: List[FeedbackItem] = []
    
    def evaluate(self, output: str, criteria: List[str]) -> EvaluationResult:
        """Evaluate output against criteria."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert evaluator for Peer Review. 
            Evaluate the output against the criteria and provide:
            1. Overall score (0.0 to 1.0)
            2. Specific feedback points
            3. Improvement suggestions"""),
            ("human", """Output to evaluate:
{output}

Criteria:
{criteria}

Provide evaluation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": output,
            "criteria": "\n".join(f"- {c}" for c in criteria)
        })
        
        content = str(response.content)
        
        # Parse response
        score = 0.75  # Default score
        if "score:" in content.lower():
            try:
                score_text = content.lower().split("score:")[1].split("\n")[0].strip()
                score = float(score_text.split()[0].replace("/1.0", "").replace(":", ""))
            except:
                pass
        
        feedback_items = [
            FeedbackItem(
                source=self.__class__.__name__,
                content=content,
                feedback_type=FeedbackType.CONSTRUCTIVE,
                score=score
            )
        ]
        
        improvements = [
            line.strip("- ").strip() 
            for line in content.split("\n") 
            if line.strip().startswith("-") or "improve" in line.lower()
        ][:3]
        
        result = EvaluationResult(
            score=score,
            feedback=feedback_items,
            improvements=improvements
        )
        
        self.history.append(result)
        self.feedback_log.extend(feedback_items)
        
        return result
    
    def provide_feedback(self, output: str) -> Dict[str, Any]:
        """Provide feedback on output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are providing constructive feedback using Peer Review pattern."),
            ("human", "{output}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": output})
        
        feedback = FeedbackItem(
            source="feedback_provider",
            content=str(response.content),
            feedback_type=FeedbackType.CONSTRUCTIVE,
            score=0.8
        )
        
        self.feedback_log.append(feedback)
        
        return {
            "feedback": feedback.content,
            "score": feedback.score,
            "type": feedback.feedback_type.value
        }
    
    def run(self, output: str, criteria: Optional[List[str]] = None) -> Dict[str, Any]:
        """Main execution method."""
        if criteria is None:
            criteria = ["clarity", "accuracy", "completeness"]
        
        result = self.evaluate(output, criteria)
        
        return {
            "pattern": "Peer Review",
            "score": result.score,
            "feedback_count": len(result.feedback),
            "improvements": result.improvements,
            "total_evaluations": len(self.history)
        }


# Example usage
if __name__ == "__main__":
    print("=== Peer Review Pattern Demo ===\n")
    
    pattern = PeerreviewPattern()
    
    # Example evaluation
    test_output = "This is a sample output that demonstrates the peer_review functionality."
    result = pattern.run(test_output, ["clarity", "usefulness", "accuracy"])
    
    print(f"Pattern: {result['pattern']}")
    print(f"Score: {result['score']:.2f}")
    print(f"Improvements suggested: {len(result['improvements'])}")
    if result['improvements']:
        print("\nSuggestions:")
        for i, imp in enumerate(result['improvements'][:3], 1):
            print(f"  {i}. {imp}")
    
    print(f"\nTotal evaluations: {result['total_evaluations']}")
    print(f"Total feedback items: {len(pattern.feedback_log)}")

Agentic Architecture Pattern: Peer Review

Pattern 213: 213 Peer Review – Line-by-Line Explanation

Overview

Pattern 213: Peer Review Pattern

Agents review each other’s work collaboratively Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

Pattern Number: 213
Pattern Name: 213 Peer Review
File Path: patterns\feedback_improvement\213_peer_review.py
Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 213: Peer Review Pattern

Agents review each other's work collaboratively
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 213 Peer Review 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

Enumerations

FeedbackType (Line 18)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    CONSTRUCTIVE
    NEUTRAL

Purpose: Types of feedback.

Values: – POSITIVE: Represents positive – NEGATIVE: Represents negative – CONSTRUCTIVE: Represents constructive – NEUTRAL: Represents neutral

Classes

FeedbackType (Line 18)

Description: Types of feedback.

FeedbackItem (Line 27)

Description: Represents a feedback item.

EvaluationResult (Line 37)

Description: Result of evaluation.

PeerreviewPattern (Line 45)

Description: Peer Review Pattern – Agents review each other’s work collaboratively

Provides peer_review capabilities for continuous improvement and quality enhancement in agentic systems.

Methods:

`init` (Line 53)

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”)

`evaluate` (Line 58)

def evaluate(self, output, criteria):
    """Evaluate output against criteria."""

Purpose: Evaluate output against criteria.

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

`provide_feedback` (Line 118)

def provide_feedback(self, output):
    """Provide feedback on output."""

Purpose: Provide feedback on output.

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

`run` (Line 143)

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

Purpose: Main execution method.

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

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 = FeedbackType(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: Self Evaluation


    """
Pattern 212: Self-Evaluation Pattern

Agent evaluates its own performance and outputs
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    CONSTRUCTIVE = "constructive"
    NEUTRAL = "neutral"


@dataclass
class FeedbackItem:
    """Represents a feedback item."""
    source: str
    content: str
    feedback_type: FeedbackType
    score: float = 0.0  # 0.0 to 1.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EvaluationResult:
    """Result of evaluation."""
    score: float
    feedback: List[FeedbackItem]
    improvements: List[str]
    timestamp: datetime = field(default_factory=datetime.now)


class SelfEvaluationPattern:
    """
    Self-Evaluation Pattern - Agent evaluates its own performance and outputs
    
    Provides self_evaluation capabilities for continuous improvement
    and quality enhancement in agentic systems.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.history: List[EvaluationResult] = []
        self.feedback_log: List[FeedbackItem] = []
    
    def evaluate(self, output: str, criteria: List[str]) -> EvaluationResult:
        """Evaluate output against criteria."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert evaluator for Self-Evaluation. 
            Evaluate the output against the criteria and provide:
            1. Overall score (0.0 to 1.0)
            2. Specific feedback points
            3. Improvement suggestions"""),
            ("human", """Output to evaluate:
{output}

Criteria:
{criteria}

Provide evaluation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": output,
            "criteria": "\n".join(f"- {c}" for c in criteria)
        })
        
        content = str(response.content)
        
        # Parse response
        score = 0.75  # Default score
        if "score:" in content.lower():
            try:
                score_text = content.lower().split("score:")[1].split("\n")[0].strip()
                score = float(score_text.split()[0].replace("/1.0", "").replace(":", ""))
            except:
                pass
        
        feedback_items = [
            FeedbackItem(
                source=self.__class__.__name__,
                content=content,
                feedback_type=FeedbackType.CONSTRUCTIVE,
                score=score
            )
        ]
        
        improvements = [
            line.strip("- ").strip() 
            for line in content.split("\n") 
            if line.strip().startswith("-") or "improve" in line.lower()
        ][:3]
        
        result = EvaluationResult(
            score=score,
            feedback=feedback_items,
            improvements=improvements
        )
        
        self.history.append(result)
        self.feedback_log.extend(feedback_items)
        
        return result
    
    def provide_feedback(self, output: str) -> Dict[str, Any]:
        """Provide feedback on output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are providing constructive feedback using Self-Evaluation pattern."),
            ("human", "{output}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": output})
        
        feedback = FeedbackItem(
            source="feedback_provider",
            content=str(response.content),
            feedback_type=FeedbackType.CONSTRUCTIVE,
            score=0.8
        )
        
        self.feedback_log.append(feedback)
        
        return {
            "feedback": feedback.content,
            "score": feedback.score,
            "type": feedback.feedback_type.value
        }
    
    def run(self, output: str, criteria: Optional[List[str]] = None) -> Dict[str, Any]:
        """Main execution method."""
        if criteria is None:
            criteria = ["clarity", "accuracy", "completeness"]
        
        result = self.evaluate(output, criteria)
        
        return {
            "pattern": "Self-Evaluation",
            "score": result.score,
            "feedback_count": len(result.feedback),
            "improvements": result.improvements,
            "total_evaluations": len(self.history)
        }


# Example usage
if __name__ == "__main__":
    print("=== Self-Evaluation Pattern Demo ===\n")
    
    pattern = SelfEvaluationPattern()
    
    # Example evaluation
    test_output = "This is a sample output that demonstrates the self_evaluation functionality."
    result = pattern.run(test_output, ["clarity", "usefulness", "accuracy"])
    
    print(f"Pattern: {result['pattern']}")
    print(f"Score: {result['score']:.2f}")
    print(f"Improvements suggested: {len(result['improvements'])}")
    if result['improvements']:
        print("\nSuggestions:")
        for i, imp in enumerate(result['improvements'][:3], 1):
            print(f"  {i}. {imp}")
    
    print(f"\nTotal evaluations: {result['total_evaluations']}")
    print(f"Total feedback items: {len(pattern.feedback_log)}")

Agentic Architecture Pattern: Self Evaluation

Pattern 212: 212 Self Evaluation – Line-by-Line Explanation

Overview

Pattern 212: Self-Evaluation Pattern

Agent evaluates its own performance and outputs Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

Pattern Number: 212
Pattern Name: 212 Self Evaluation
File Path: patterns\feedback_improvement\212_self_evaluation.py
Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 212: Self-Evaluation Pattern

Agent evaluates its own performance and outputs
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 212 Self Evaluation pattern.

Imports Section

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

Enumerations

FeedbackType (Line 18)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    CONSTRUCTIVE
    NEUTRAL

Purpose: Types of feedback.

Values: – POSITIVE: Represents positive – NEGATIVE: Represents negative – CONSTRUCTIVE: Represents constructive – NEUTRAL: Represents neutral

Classes

FeedbackType (Line 18)

Description: Types of feedback.

FeedbackItem (Line 27)

Description: Represents a feedback item.

EvaluationResult (Line 37)

Description: Result of evaluation.

SelfEvaluationPattern (Line 45)

Description: Self-Evaluation Pattern – Agent evaluates its own performance and outputs

Provides self_evaluation capabilities for continuous improvement and quality enhancement in agentic systems.

Methods:

`init` (Line 53)

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”)

`evaluate` (Line 58)

def evaluate(self, output, criteria):
    """Evaluate output against criteria."""

Purpose: Evaluate output against criteria.

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

`provide_feedback` (Line 118)

def provide_feedback(self, output):
    """Provide feedback on output."""

Purpose: Provide feedback on output.

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

`run` (Line 143)

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

Purpose: Main execution method.

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

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 = FeedbackType(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: Feedback Loop


    """
Pattern 211: Feedback Loop Pattern

Implements continuous improvement through iterative feedback cycles.
Features feedback collection, analysis, and action application.
"""

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


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    NEUTRAL = "neutral"
    SUGGESTION = "suggestion"


@dataclass
class Feedback:
    """Represents feedback from evaluation."""
    feedback_id: str
    content: str
    feedback_type: FeedbackType
    score: float  # -1.0 to 1.0
    source: str
    iteration: int
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Iteration:
    """Represents one iteration in the feedback loop."""
    iteration_number: int
    input_data: Any
    output: Any
    feedbacks: List[Feedback] = field(default_factory=list)
    improvements_applied: List[str] = field(default_factory=list)
    quality_score: float = 0.0
    timestamp: datetime = field(default_factory=datetime.now)


class FeedbackCollector:
    """Collects and analyzes feedback."""
    
    def __init__(self, llm: ChatOpenAI):
        self.llm = llm
        self.all_feedback: List[Feedback] = []
    
    def collect_feedback(self, output: Any, iteration: int) -> List[Feedback]:
        """Collect feedback on output using LLM."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a critic providing constructive feedback. "
                      "Analyze the output and provide specific, actionable feedback."),
            ("human", """Output to evaluate:
{output}

Provide feedback in the following format:
1. List 2-3 specific strengths
2. List 2-3 specific areas for improvement  
3. Overall quality score (0.0 to 1.0)

Be specific and actionable.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": str(output)})
        
        # Parse feedback (simplified)
        feedback_items = []
        
        # Positive feedback
        feedback_items.append(Feedback(
            feedback_id=f"fb_{iteration}_pos",
            content=f"Positive aspects from iteration {iteration}",
            feedback_type=FeedbackType.POSITIVE,
            score=0.7,
            source="llm_critic",
            iteration=iteration
        ))
        
        # Improvement suggestions
        feedback_items.append(Feedback(
            feedback_id=f"fb_{iteration}_improve",
            content=response.content,
            feedback_type=FeedbackType.SUGGESTION,
            score=0.0,
            source="llm_critic",
            iteration=iteration
        ))
        
        self.all_feedback.extend(feedback_items)
        return feedback_items


class FeedbackLoopAgent:
    """Agent that improves through feedback loops."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.feedback_collector = FeedbackCollector(self.llm)
        self.iterations: List[Iteration] = []
        self.current_best_output: Optional[Any] = None
        self.best_score: float = 0.0
    
    def execute_with_feedback(self, task: str, max_iterations: int = 3) -> Dict[str, Any]:
        """Execute task with iterative feedback and improvement."""
        print(f"\n   {self.name}: Starting feedback loop for task")
        print(f"   Max iterations: {max_iterations}")
        
        current_input = task
        
        for iteration_num in range(1, max_iterations + 1):
            print(f"\n   === Iteration {iteration_num}/{max_iterations} ===")
            
            # Generate output
            output = self._generate_output(current_input, iteration_num)
            print(f"   Output generated: {str(output)[:80]}...")
            
            # Collect feedback
            feedbacks = self.feedback_collector.collect_feedback(output, iteration_num)
            print(f"   Collected {len(feedbacks)} feedback items")
            
            # Calculate quality score
            quality_score = self._calculate_quality(feedbacks)
            print(f"   Quality score: {quality_score:.3f}")
            
            # Record iteration
            improvements = self._identify_improvements(feedbacks)
            iteration = Iteration(
                iteration_number=iteration_num,
                input_data=current_input,
                output=output,
                feedbacks=feedbacks,
                improvements_applied=improvements,
                quality_score=quality_score
            )
            self.iterations.append(iteration)
            
            # Update best output
            if quality_score > self.best_score:
                self.best_score = quality_score
                self.current_best_output = output
                print(f"   ✓ New best output!")
            
            # Check convergence
            if quality_score >= 0.9:
                print(f"   ✓ Quality threshold reached!")
                break
            
            # Apply improvements for next iteration
            if iteration_num  str:
        """Generate output for current iteration."""
        context = ""
        if iteration > 1:
            prev_feedback = self.iterations[-1].feedbacks
            context = f"\nPrevious feedback: {prev_feedback[0].content[:200] if prev_feedback else 'None'}"
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}. Generate high-quality output. "
                      f"This is iteration {iteration}. Apply previous feedback if available."),
            ("human", "Task: {task}{context}\n\nGenerate output:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"task": str(input_data), "context": context})
        return response.content
    
    def _calculate_quality(self, feedbacks: List[Feedback]) -> float:
        """Calculate overall quality score from feedback."""
        if not feedbacks:
            return 0.5
        
        # Simple average of feedback scores (normalized to 0-1)
        positive_count = sum(1 for f in feedbacks if f.feedback_type == FeedbackType.POSITIVE)
        total = len(feedbacks)
        return min(1.0, 0.3 + (positive_count / total) * 0.7)
    
    def _identify_improvements(self, feedbacks: List[Feedback]) -> List[str]:
        """Identify improvements from feedback."""
        improvements = []
        for feedback in feedbacks:
            if feedback.feedback_type in [FeedbackType.NEGATIVE, FeedbackType.SUGGESTION]:
                improvements.append(f"Address: {feedback.content[:50]}...")
        return improvements
    
    def _apply_improvements(self, original_task: str, feedbacks: List[Feedback], 
                           previous_output: Any) -> str:
        """Apply improvements for next iteration."""
        suggestions = [f.content for f in feedbacks 
                      if f.feedback_type == FeedbackType.SUGGESTION]
        
        improved_task = f"{original_task}\n\nPrevious attempt had these issues:\n"
        improved_task += "\n".join(suggestions[:2])  # Top 2 suggestions
        
        return improved_task


class FeedbackLoopPattern:
    """
    Feedback Loop Pattern - Continuous improvement through feedback.
    
    Implements iterative refinement where agents improve their outputs
    based on collected feedback.
    """
    
    def __init__(self):
        self.agents: Dict[str, FeedbackLoopAgent] = {}
    
    def register_agent(self, agent: FeedbackLoopAgent):
        """Register feedback loop agent."""
        self.agents[agent.name] = agent
    
    def run(self, tasks: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Execute feedback loop on tasks."""
        print("=== Feedback Loop Pattern Demo ===\n")
        
        results = []
        for task_info in tasks:
            print(f"\nTask: {task_info.get('description', 'Unknown')}")
            
            agent_name = task_info.get("agent")
            if agent_name not in self.agents:
                continue
            
            agent = self.agents[agent_name]
            task = task_info.get("task")
            max_iterations = task_info.get("max_iterations", 3)
            
            result = agent.execute_with_feedback(task, max_iterations)
            results.append(result)
        
        # Statistics
        total_iterations = sum(r["total_iterations"] for r in results)
        avg_score = sum(r["best_score"] for r in results) / len(results) if results else 0
        
        return {
            "pattern": "FeedbackLoop",
            "total_agents": len(self.agents),
            "tasks_completed": len(results),
            "total_iterations": total_iterations,
            "average_best_score": avg_score
        }


# Example usage
if __name__ == "__main__":
    print("=== Feedback Loop Pattern Demo ===\n")
    
    # Create pattern
    pattern = FeedbackLoopPattern()
    
    # Create agent
    print("1. Creating feedback loop agent...")
    agent = FeedbackLoopAgent("WriterAgent")
    pattern.register_agent(agent)
    
    # Define tasks
    print("\n2. Defining tasks for iterative improvement...")
    
    tasks = [
        {
            "description": "Write product description",
            "agent": "WriterAgent",
            "task": "Write a compelling product description for a smart watch",
            "max_iterations": 3
        },
        {
            "description": "Create email subject line",
            "agent": "WriterAgent",
            "task": "Create an engaging email subject line for a summer sale",
            "max_iterations": 2
        }
    ]
    
    # Execute
    print("\n3. Executing with feedback loops...")
    summary = pattern.run(tasks)
    
    print(f"\n4. Summary:")
    print(f"   Tasks Completed: {summary['tasks_completed']}")
    print(f"   Total Iterations: {summary['total_iterations']}")
    print(f"   Average Best Score: {summary['average_best_score']:.3f}")
    
    print("\n5. Iteration History:")
    for i, iteration in enumerate(agent.iterations, 1):
        print(f"   Iteration {iteration.iteration_number}:")
        print(f"     Quality: {iteration.quality_score:.3f}")
        print(f"     Improvements: {len(iteration.improvements_applied)}")
        print(f"     Feedback items: {len(iteration.feedbacks)}")
    
    print(f"\n6. Best Output:")
    if agent.current_best_output:
        print(f"   {str(agent.current_best_output)[:200]}...")
        print(f"   Score: {agent.best_score:.3f}")
    
    print("\n✓ Feedback loop enabled continuous improvement!")

Agentic Architecture Pattern: Feedback Loop

Pattern 211: 211 Feedback Loop – Line-by-Line Explanation

Overview

Pattern 211: Feedback Loop Pattern

Implements continuous improvement through iterative feedback cycles. Features feedback collection, analysis, and action application.

Code Structure Analysis

File Information

Pattern Number: 211
Pattern Name: 211 Feedback Loop
File Path: patterns\feedback_improvement\211_feedback_loop.py
Total Lines: 314

Detailed Code Explanation

Module Docstring

"""
Pattern 211: Feedback Loop Pattern

Implements continuous improvement through iterative feedback cycles.
Features feedback collection, analysis, and action application.
"""

Explanation: This module docstring describes the purpose and key features of the 211 Feedback Loop 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

FeedbackType (Line 16)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    NEUTRAL
    SUGGESTION

Purpose: Types of feedback.

Values: – POSITIVE: Represents positive – NEGATIVE: Represents negative – NEUTRAL: Represents neutral – SUGGESTION: Represents suggestion

Classes

FeedbackType (Line 16)

Description: Types of feedback.

Feedback (Line 25)

Description: Represents feedback from evaluation.

Iteration (Line 37)

Description: Represents one iteration in the feedback loop.

FeedbackCollector (Line 48)

Description: Collects and analyzes feedback.

Methods:

`init` (Line 51)

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

Purpose: No method docstring.

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

`collect_feedback` (Line 55)

def collect_feedback(self, output, iteration):
    """Collect feedback on output using LLM."""

Purpose: Collect feedback on output using LLM.

Parameters: – self: Instance reference – output: Parameter for output – iteration: Parameter for iteration

FeedbackLoopAgent (Line 101)

Description: Agent that improves through feedback loops.

Methods:

`init` (Line 104)

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

Purpose: No method docstring.

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

`execute_with_feedback` (Line 112)

def execute_with_feedback(self, task, max_iterations):
    """Execute task with iterative feedback and improvement."""

Purpose: Execute task with iterative feedback and improvement.

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

`_generate_output` (Line 168)

def _generate_output(self, input_data, iteration):
    """Generate output for current iteration."""

Purpose: Generate output for current iteration.

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

`_calculate_quality` (Line 185)

def _calculate_quality(self, feedbacks):
    """Calculate overall quality score from feedback."""

Purpose: Calculate overall quality score from feedback.

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

`_identify_improvements` (Line 195)

def _identify_improvements(self, feedbacks):
    """Identify improvements from feedback."""

Purpose: Identify improvements from feedback.

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

`_apply_improvements` (Line 203)

def _apply_improvements(self, original_task, feedbacks, previous_output):
    """Apply improvements for next iteration."""

Purpose: Apply improvements for next iteration.

Parameters: – self: Instance reference – original_task: Parameter for original_task – feedbacks: Parameter for feedbacks – previous_output: Parameter for previous_output

FeedbackLoopPattern (Line 215)

Description: Feedback Loop Pattern – Continuous improvement through feedback.

Implements iterative refinement where agents improve their outputs based on collected feedback.

Methods:

`init` (Line 223)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

`register_agent` (Line 226)

def register_agent(self, agent):
    """Register feedback loop agent."""

Purpose: Register feedback loop agent.

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

`run` (Line 230)

def run(self, tasks):
    """Execute feedback loop on tasks."""

Purpose: Execute feedback loop on tasks.

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

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 = FeedbackType(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: Counterfactual Reasoning


    """
Pattern 210: Counterfactual Reasoning Pattern

Reasons about "what if" scenarios and alternative possibilities.
Explores hypothetical situations contrary to actual facts.
"""

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


class CounterfactualType(Enum):
    """Types of counterfactual reasoning."""
    WHAT_IF = "what_if"  # What if X had happened?
    IF_ONLY = "if_only"  # If only X had been different
    ALTERNATIVE = "alternative"  # Alternative possible outcomes
    MINIMAL_CHANGE = "minimal_change"  # Minimal change for different outcome


@dataclass
class ActualWorld:
    """The actual state of affairs."""
    facts: List[str]
    outcome: str
    context: Dict[str, Any] = field(default_factory=dict)


@dataclass
class CounterfactualWorld:
    """A hypothetical alternative world."""
    change: str  # What's different from actual
    predicted_facts: List[str]
    predicted_outcome: str
    plausibility: float = 0.5  # 0.0 to 1.0
    reasoning: str = ""


@dataclass
class CounterfactualQuery:
    """Query about counterfactual scenario."""
    actual_world: ActualWorld
    counterfactual_change: str
    question: str


class CounterfactualReasoningAgent:
    """Agent that performs counterfactual reasoning."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.scenarios: List[CounterfactualWorld] = []
    
    def generate_counterfactual(self, actual: ActualWorld, 
                               change: str) -> CounterfactualWorld:
        """Generate counterfactual scenario with change."""
        print(f"\n  {self.name}: Generating counterfactual: '{change}'...")
        
        actual_facts = "\n".join([f"- {fact}" for fact in actual.facts])
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a counterfactual reasoning expert. Imagine alternative "
                      "scenarios and predict consequences."),
            ("human", """Actual World:
Facts: 
{facts}
Outcome: {outcome}

Counterfactual Change: {change}

If this change had occurred, what would be different?
Provide:
1. New facts (what changes)
2. Alternative outcome
3. Plausibility (0.0-1.0)
4. Reasoning chain

Format:
FACTS: 
OUTCOME: 
PLAUSIBILITY: 
REASONING: """)
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "facts": actual_facts,
            "outcome": actual.outcome,
            "change": change
        })
        
        # Parse response
        new_facts = []
        new_outcome = ""
        plausibility = 0.5
        reasoning = ""
        
        current_section = None
        for line in response.content.split("\n"):
            line = line.strip()
            if line.startswith("FACTS:"):
                current_section = "facts"
                continue
            elif line.startswith("OUTCOME:"):
                new_outcome = line.replace("OUTCOME:", "").strip()
                current_section = "outcome"
                continue
            elif line.startswith("PLAUSIBILITY:"):
                try:
                    plausibility = float(line.replace("PLAUSIBILITY:", "").strip())
                except:
                    plausibility = 0.5
                current_section = "plausibility"
                continue
            elif line.startswith("REASONING:"):
                current_section = "reasoning"
                reasoning = line.replace("REASONING:", "").strip()
                continue
            
            if current_section == "facts" and line.startswith("-"):
                new_facts.append(line.lstrip("- "))
            elif current_section == "reasoning" and line:
                reasoning += " " + line
        
        counterfactual = CounterfactualWorld(
            change=change,
            predicted_facts=new_facts if new_facts else ["Unknown changes"],
            predicted_outcome=new_outcome or "Unknown outcome",
            plausibility=plausibility,
            reasoning=reasoning.strip() or "Hypothetical alternative"
        )
        
        self.scenarios.append(counterfactual)
        
        print(f"  Predicted outcome: {counterfactual.predicted_outcome[:80]}...")
        print(f"  Plausibility: {counterfactual.plausibility}")
        
        return counterfactual
    
    def compare_worlds(self, actual: ActualWorld, 
                      counterfactual: CounterfactualWorld) -> Dict[str, Any]:
        """Compare actual and counterfactual worlds."""
        print(f"\n  {self.name}: Comparing worlds...")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Compare actual and counterfactual scenarios."),
            ("human", """Actual Outcome: {actual_outcome}
Counterfactual Outcome: {counterfactual_outcome}

What are the key differences? What insights does this comparison reveal?""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "actual_outcome": actual.outcome,
            "counterfactual_outcome": counterfactual.predicted_outcome
        })
        
        comparison = {
            "actual": actual.outcome,
            "counterfactual": counterfactual.predicted_outcome,
            "analysis": response.content,
            "change": counterfactual.change
        }
        
        return comparison
    
    def identify_critical_factors(self, actual: ActualWorld) -> List[str]:
        """Identify factors critical to the actual outcome."""
        print(f"\n  {self.name}: Identifying critical factors...")
        
        facts_text = "\n".join(actual.facts)
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Identify which factors were most critical to the outcome."),
            ("human", """Facts:
{facts}

Outcome: {outcome}

Which factors were most critical? List 2-3 key factors.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "facts": facts_text,
            "outcome": actual.outcome
        })
        
        factors = [line.strip().lstrip("0123456789.-) ")
                  for line in response.content.split("\n")
                  if line.strip()]
        
        print(f"  Identified {len(factors)} critical factors")
        return factors[:3]
    
    def explore_minimal_changes(self, actual: ActualWorld, 
                               desired_outcome: str) -> CounterfactualWorld:
        """Find minimal change needed for desired outcome."""
        print(f"\n  {self.name}: Finding minimal change for desired outcome...")
        
        facts_text = "\n".join(actual.facts)
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Find the minimal change that would lead to desired outcome."),
            ("human", """Current Facts:
{facts}

Current Outcome: {current_outcome}
Desired Outcome: {desired_outcome}

What is the SMALLEST change that would lead to the desired outcome?
Explain the change and why it would work.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "facts": facts_text,
            "current_outcome": actual.outcome,
            "desired_outcome": desired_outcome
        })
        
        minimal_change = CounterfactualWorld(
            change=response.content.split("\n")[0],
            predicted_facts=[],
            predicted_outcome=desired_outcome,
            plausibility=0.7,
            reasoning=response.content
        )
        
        print(f"  Minimal change: {minimal_change.change[:80]}...")
        
        return minimal_change


class CounterfactualReasoningPattern:
    """
    Counterfactual Reasoning Pattern - Explore "what if" scenarios.
    
    Generates alternative scenarios, compares outcomes, identifies
    critical factors, and explores minimal changes.
    """
    
    def __init__(self):
        self.agents: Dict[str, CounterfactualReasoningAgent] = {}
    
    def register_agent(self, agent: CounterfactualReasoningAgent):
        """Register counterfactual reasoning agent."""
        self.agents[agent.name] = agent
    
    def run(self, queries: List[CounterfactualQuery]) -> Dict[str, Any]:
        """Execute counterfactual reasoning queries."""
        print("=== Counterfactual Reasoning Pattern ===\n")
        
        comparisons = []
        for agent in self.agents.values():
            for query in queries:
                # Generate counterfactual
                counterfactual = agent.generate_counterfactual(
                    query.actual_world,
                    query.counterfactual_change
                )
                
                # Compare worlds
                comparison = agent.compare_worlds(
                    query.actual_world,
                    counterfactual
                )
                comparisons.append(comparison)
        
        return {
            "pattern": "CounterfactualReasoning",
            "agents": len(self.agents),
            "queries_processed": len(queries),
            "scenarios_explored": sum(len(a.scenarios) for a in self.agents.values()),
            "comparisons": comparisons
        }


# Example usage
if __name__ == "__main__":
    print("=== Counterfactual Reasoning Pattern Demo ===\n")
    
    # Create pattern and agent
    pattern = CounterfactualReasoningPattern()
    agent = CounterfactualReasoningAgent("WhatIfAnalyst")
    pattern.register_agent(agent)
    
    # Actual world scenarios
    print("1. Defining actual world scenarios...\n")
    
    # Scenario 1: Historical event
    actual1 = ActualWorld(
        facts=[
            "Columbus sailed west in 1492",
            "He had support from Spanish monarchy",
            "Navigation technology was available"
        ],
        outcome="European discovery of the Americas in 1492"
    )
    
    # Scenario 2: Business decision
    actual2 = ActualWorld(
        facts=[
            "Company invested heavily in mobile apps",
            "Market shifted to mobile-first",
            "Competitors were slow to adapt"
        ],
        outcome="Company became market leader in mobile space"
    )
    
    # Counterfactual queries
    print("2. Exploring counterfactual scenarios...\n")
    queries = [
        CounterfactualQuery(
            actual_world=actual1,
            counterfactual_change="What if the Spanish monarchy had refused funding?",
            question="Would the Americas still be discovered in 1492?"
        ),
        CounterfactualQuery(
            actual_world=actual2,
            counterfactual_change="What if the company had focused on desktop instead?",
            question="Would they still be market leader?"
        )
    ]
    
    # Run counterfactual reasoning
    summary = pattern.run(queries)
    
    print(f"\n=== Summary ===")
    print(f"Queries processed: {summary['queries_processed']}")
    print(f"Scenarios explored: {summary['scenarios_explored']}")
    
    print(f"\n=== Counterfactual Analysis ===")
    for i, comp in enumerate(summary['comparisons'], 1):
        print(f"\nScenario {i}:")
        print(f"  Change: {comp['change'][:60]}...")
        print(f"  Actual: {comp['actual'][:60]}...")
        print(f"  Counterfactual: {comp['counterfactual'][:60]}...")
    
    # Additional: Identify critical factors
    print(f"\n=== Critical Factors ===")
    critical = agent.identify_critical_factors(actual1)
    for factor in critical:
        print(f"  - {factor}")
    
    print("\n✓ Counterfactual reasoning: exploring alternative realities!")

Agentic Architecture Pattern: Counterfactual Reasoning

Pattern 210: 210 Counterfactual Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 210: Counterfactual Reasoning Pattern

Reasons about “what if” scenarios and alternative possibilities. Explores hypothetical situations contrary to actual facts.

Code Structure Analysis

File Information

Pattern Number: 210
Pattern Name: 210 Counterfactual Reasoning Pattern
File Path: patterns\advanced_reasoning\210_counterfactual_reasoning_pattern.py
Total Lines: 352

Detailed Code Explanation

Module Docstring

"""
Pattern 210: Counterfactual Reasoning Pattern

Reasons about "what if" scenarios and alternative possibilities.
Explores hypothetical situations contrary to actual facts.
"""

Explanation: This module docstring describes the purpose and key features of the 210 Counterfactual Reasoning 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  # First 20 imports shown

Enumerations

CounterfactualType (Line 16)

class CounterfactualType(Enum):
    WHAT_IF
    IF_ONLY
    ALTERNATIVE
    MINIMAL_CHANGE

Purpose: Types of counterfactual reasoning.

Values: – WHAT_IF: Represents what if – IF_ONLY: Represents if only – ALTERNATIVE: Represents alternative – MINIMAL_CHANGE: Represents minimal change

Classes

CounterfactualType (Line 16)

Description: Types of counterfactual reasoning.

ActualWorld (Line 25)

Description: The actual state of affairs.

CounterfactualWorld (Line 33)

Description: A hypothetical alternative world.

CounterfactualQuery (Line 43)

Description: Query about counterfactual scenario.

CounterfactualReasoningAgent (Line 50)

Description: Agent that performs counterfactual reasoning.

Methods:

`init` (Line 53)

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

Purpose: No method docstring.

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

`generate_counterfactual` (Line 58)

def generate_counterfactual(self, actual, change):
    """Generate counterfactual scenario with change."""

Purpose: Generate counterfactual scenario with change.

Parameters: – self: Instance reference – actual: Parameter for actual – change: Parameter for change

`compare_worlds` (Line 144)

def compare_worlds(self, actual, counterfactual):
    """Compare actual and counterfactual worlds."""

Purpose: Compare actual and counterfactual worlds.

Parameters: – self: Instance reference – actual: Parameter for actual – counterfactual: Parameter for counterfactual

`identify_critical_factors` (Line 172)

def identify_critical_factors(self, actual):
    """Identify factors critical to the actual outcome."""

Purpose: Identify factors critical to the actual outcome.

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

`explore_minimal_changes` (Line 201)

def explore_minimal_changes(self, actual, desired_outcome):
    """Find minimal change needed for desired outcome."""

Purpose: Find minimal change needed for desired outcome.

Parameters: – self: Instance reference – actual: Parameter for actual – desired_outcome: Parameter for desired_outcome

CounterfactualReasoningPattern (Line 240)

Description: Counterfactual Reasoning Pattern – Explore “what if” scenarios.

Generates alternative scenarios, compares outcomes, identifies critical factors, and explores minimal changes.

Methods:

`init` (Line 248)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

`register_agent` (Line 251)

def register_agent(self, agent):
    """Register counterfactual reasoning agent."""

Purpose: Register counterfactual reasoning agent.

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

`run` (Line 255)

def run(self, queries):
    """Execute counterfactual reasoning queries."""

Purpose: Execute counterfactual reasoning queries.

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

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 = CounterfactualType(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

Author: admin

Agentic Architecture Pattern: Experimentation

Agentic Architecture Pattern: Experimentation

Pattern 219: 219 Experimentation – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Enumerations

FeedbackType (Line 18)

Classes

FeedbackType (Line 18)

FeedbackItem (Line 27)

EvaluationResult (Line 37)

ExperimentationPattern (Line 45)

__init__ (Line 53)

evaluate (Line 58)

provide_feedback (Line 118)

run (Line 143)

Design Patterns Used

Usage Example

Key Concepts

Extension Points

Agentic Architecture Pattern: Ab Testing

Agentic Architecture Pattern: Ab Testing

Pattern 218: 218 Ab Testing – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Design Patterns Used

Usage Example

Key Concepts

Extension Points

Agentic Architecture Pattern: Continuous Improvement

Agentic Architecture Pattern: Continuous Improvement

Pattern 217: 217 Continuous Improvement – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Enumerations

FeedbackType (Line 18)

Classes

FeedbackType (Line 18)

FeedbackItem (Line 27)

EvaluationResult (Line 37)

ContinuousimprovementPattern (Line 45)

__init__ (Line 53)

evaluate (Line 58)

provide_feedback (Line 118)

run (Line 143)

Design Patterns Used

Usage Example

Key Concepts

Extension Points

Agentic Architecture Pattern: Quality Assurance

Agentic Architecture Pattern: Quality Assurance

Pattern 216: 216 Quality Assurance – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Enumerations

FeedbackType (Line 18)

Classes

FeedbackType (Line 18)

FeedbackItem (Line 27)

EvaluationResult (Line 37)

QualityassurancePattern (Line 45)

__init__ (Line 53)

evaluate (Line 58)

`init` (Line 53)

`evaluate` (Line 58)

`provide_feedback` (Line 118)

`run` (Line 143)

`init` (Line 53)

`evaluate` (Line 58)

`provide_feedback` (Line 118)

`run` (Line 143)

`init` (Line 53)

`evaluate` (Line 58)

`provide_feedback` (Line 118)

`run` (Line 143)

`init` (Line 53)

`evaluate` (Line 58)

`provide_feedback` (Line 118)

`run` (Line 143)

`init` (Line 53)

`evaluate` (Line 58)

`provide_feedback` (Line 118)

`run` (Line 143)