Tag: agentic

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

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

Enumerations

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

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: Logical Reasoning


    """
Pattern 209: Logical Reasoning Pattern

Applies formal logic, propositional and predicate logic to derive conclusions.
Handles logical operators (AND, OR, NOT), quantifiers, and logical proofs.
"""

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


class LogicalOperator(Enum):
    """Logical operators."""
    AND = "AND"
    OR = "OR"
    NOT = "NOT"
    IMPLIES = "=>"
    IFF = ""  # if and only if


@dataclass
class Proposition:
    """Logical proposition."""
    statement: str
    truth_value: Optional[bool] = None
    variables: Set[str] = field(default_factory=set)


@dataclass
class LogicalRule:
    """Logical inference rule."""
    premises: List[str]
    conclusion: str
    rule_name: str = "modus_ponens"


@dataclass
class LogicalArgument:
    """Complete logical argument."""
    premises: List[Proposition]
    conclusion: Proposition
    is_valid: Optional[bool] = None
    proof_steps: List[str] = field(default_factory=list)


class LogicalReasoningAgent:
    """Agent that performs logical reasoning."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.1)  # Low temp for logic
        self.knowledge_base: List[Proposition] = []
        self.rules: List[LogicalRule] = []
    
    def add_proposition(self, prop: Proposition):
        """Add proposition to knowledge base."""
        self.knowledge_base.append(prop)
        print(f"  Added: {prop.statement} = {prop.truth_value}")
    
    def evaluate_logical_expression(self, expression: str, 
                                    context: Dict[str, bool]) -> bool:
        """Evaluate logical expression given variable values."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Evaluate logical expression. Answer only TRUE or FALSE."),
            ("human", """Expression: {expression}
Variable values: {context}

What is the truth value? (TRUE or FALSE)""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "expression": expression,
            "context": str(context)
        })
        
        return "TRUE" in response.content.upper()
    
    def check_validity(self, argument: LogicalArgument) -> bool:
        """Check if logical argument is valid."""
        print(f"\n  {self.name}: Checking argument validity...")
        
        premises_text = "\n".join([f"- {p.statement}" for p in argument.premises])
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a logic expert. Determine if the conclusion follows "
                      "logically from the premises."),
            ("human", """Premises:
{premises}

Conclusion: {conclusion}

Is this argument logically valid? Answer YES or NO and explain why.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "premises": premises_text,
            "conclusion": argument.conclusion.statement
        })
        
        is_valid = "YES" in response.content.split("\n")[0].upper()
        argument.is_valid = is_valid
        
        print(f"  Argument is {'valid' if is_valid else 'invalid'}")
        return is_valid
    
    def construct_proof(self, premises: List[str], conclusion: str) -> List[str]:
        """Construct logical proof from premises to conclusion."""
        print(f"\n  {self.name}: Constructing proof...")
        
        premises_text = "\n".join([f"{i+1}. {p}" for i, p in enumerate(premises)])
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Construct a step-by-step logical proof."),
            ("human", """Given premises:
{premises}

Prove: {conclusion}

Provide proof steps, each on a new line, showing logical progression.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "premises": premises_text,
            "conclusion": conclusion
        })
        
        proof_steps = [line.strip() for line in response.content.split("\n") 
                      if line.strip() and not line.strip().startswith("Prove")]
        
        print(f"  Proof constructed with {len(proof_steps)} steps")
        return proof_steps
    
    def apply_modus_ponens(self, conditional: str, antecedent: str) -> Optional[str]:
        """Apply modus ponens: If P then Q, P, therefore Q."""
        print(f"\n  Applying modus ponens...")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Apply modus ponens rule."),
            ("human", """Rule: {conditional}
Fact: {antecedent}

What conclusion follows? (Just state the conclusion)""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "conditional": conditional,
            "antecedent": antecedent
        })
        
        conclusion = response.content.strip()
        print(f"  Conclusion: {conclusion}")
        return conclusion
    
    def check_consistency(self) -> bool:
        """Check if knowledge base is logically consistent."""
        if len(self.knowledge_base)  str:
        """Solve logic puzzle using reasoning."""
        print(f"\n  {self.name}: Solving logic puzzle...")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Solve this logic puzzle step-by-step using logical reasoning."),
            ("human", "{puzzle}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"puzzle": puzzle})
        
        return response.content


class LogicalReasoningPattern:
    """
    Logical Reasoning Pattern - Apply formal logic.
    
    Handles logical operators, validates arguments, constructs proofs,
    and applies inference rules.
    """
    
    def __init__(self):
        self.agents: Dict[str, LogicalReasoningAgent] = {}
    
    def register_agent(self, agent: LogicalReasoningAgent):
        """Register logical reasoning agent."""
        self.agents[agent.name] = agent
    
    def run(self, scenarios: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Execute logical reasoning scenarios."""
        print("=== Logical Reasoning Pattern ===\n")
        
        results = []
        for agent in self.agents.values():
            for scenario in scenarios:
                scenario_type = scenario.get("type", "argument")
                
                if scenario_type == "argument":
                    argument = LogicalArgument(
                        premises=[Proposition(p) for p in scenario["premises"]],
                        conclusion=Proposition(scenario["conclusion"])
                    )
                    is_valid = agent.check_validity(argument)
                    results.append({"type": "argument", "valid": is_valid})
                
                elif scenario_type == "proof":
                    proof = agent.construct_proof(
                        scenario["premises"],
                        scenario["conclusion"]
                    )
                    results.append({"type": "proof", "steps": len(proof)})
                
                elif scenario_type == "modus_ponens":
                    conclusion = agent.apply_modus_ponens(
                        scenario["conditional"],
                        scenario["antecedent"]
                    )
                    results.append({"type": "inference", "conclusion": conclusion})
                
                elif scenario_type == "puzzle":
                    solution = agent.solve_logic_puzzle(scenario["puzzle"])
                    results.append({"type": "puzzle", "solution": solution[:100]})
        
        return {
            "pattern": "LogicalReasoning",
            "agents": len(self.agents),
            "scenarios_processed": len(scenarios),
            "results": results
        }


# Example usage
if __name__ == "__main__":
    print("=== Logical Reasoning Pattern Demo ===\n")
    
    # Create pattern and agent
    pattern = LogicalReasoningPattern()
    agent = LogicalReasoningAgent("Logician")
    pattern.register_agent(agent)
    
    # Logical reasoning scenarios
    print("1. Testing logical reasoning capabilities...\n")
    scenarios = [
        {
            "type": "argument",
            "premises": [
                "All humans are mortal",
                "Socrates is a human"
            ],
            "conclusion": "Socrates is mortal"
        },
        {
            "type": "proof",
            "premises": [
                "If it rains, the ground gets wet",
                "It is raining"
            ],
            "conclusion": "The ground is wet"
        },
        {
            "type": "modus_ponens",
            "conditional": "If it's sunny, we go to the beach",
            "antecedent": "It's sunny"
        },
        {
            "type": "puzzle",
            "puzzle": "Three people: Alice, Bob, and Carol. One always tells truth, "
                     "one always lies, one alternates. Alice says 'I am not the liar'. "
                     "Bob says 'Alice is the liar'. Who is who?"
        }
    ]
    
    # Run logical reasoning
    summary = pattern.run(scenarios)
    
    print(f"\n=== Summary ===")
    print(f"Scenarios processed: {summary['scenarios_processed']}")
    print(f"Results: {len(summary['results'])} logical operations completed")
    
    print("\n✓ Logical reasoning: applying formal logic!")

Agentic Architecture Pattern: Logical Reasoning

Pattern 209: 209 Logical Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 209: Logical Reasoning Pattern

Applies formal logic, propositional and predicate logic to derive conclusions. Handles logical operators (AND, OR, NOT), quantifiers, and logical proofs.

Code Structure Analysis

File Information

Pattern Number: 209
Pattern Name: 209 Logical Reasoning Pattern
File Path: patterns\advanced_reasoning\209_logical_reasoning_pattern.py
Total Lines: 304

Detailed Code Explanation

Module Docstring

"""
Pattern 209: Logical Reasoning Pattern

Applies formal logic, propositional and predicate logic to derive conclusions.
Handles logical operators (AND, OR, NOT), quantifiers, and logical proofs.
"""

Explanation: This module docstring describes the purpose and key features of the 209 Logical Reasoning Pattern pattern.

Imports Section

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

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

Enumerations

LogicalOperator (Line 15)

class LogicalOperator(Enum):
    AND
    OR
    NOT
    IMPLIES
    IFF

Purpose: Logical operators.

Values: – AND: Represents and – OR: Represents or – NOT: Represents not – IMPLIES: Represents implies – IFF: Represents iff

Classes

LogicalOperator (Line 15)

Description: Logical operators.

Proposition (Line 25)

Description: Logical proposition.

LogicalRule (Line 33)

Description: Logical inference rule.

LogicalArgument (Line 41)

Description: Complete logical argument.

LogicalReasoningAgent (Line 49)

Description: Agent that performs logical reasoning.

Methods:

`init` (Line 52)

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”)

`add_proposition` (Line 58)

def add_proposition(self, prop):
    """Add proposition to knowledge base."""

Purpose: Add proposition to knowledge base.

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

`evaluate_logical_expression` (Line 63)

def evaluate_logical_expression(self, expression, context):
    """Evaluate logical expression given variable values."""

Purpose: Evaluate logical expression given variable values.

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

`check_validity` (Line 82)

def check_validity(self, argument):
    """Check if logical argument is valid."""

Purpose: Check if logical argument is valid.

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

`construct_proof` (Line 111)

def construct_proof(self, premises, conclusion):
    """Construct logical proof from premises to conclusion."""

Purpose: Construct logical proof from premises to conclusion.

Parameters: – self: Instance reference – premises: Parameter for premises – conclusion: Parameter for conclusion

`apply_modus_ponens` (Line 139)

def apply_modus_ponens(self, conditional, antecedent):
    """Apply modus ponens: If P then Q, P, therefore Q."""

Purpose: Apply modus ponens: If P then Q, P, therefore Q.

Parameters: – self: Instance reference – conditional: Parameter for conditional – antecedent: Parameter for antecedent

`check_consistency` (Line 161)

def check_consistency(self):
    """Check if knowledge base is logically consistent."""

Purpose: Check if knowledge base is logically consistent.

Parameters: – self: Instance reference

`solve_logic_puzzle` (Line 182)

def solve_logic_puzzle(self, puzzle):
    """Solve logic puzzle using reasoning."""

Purpose: Solve logic puzzle using reasoning.

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

LogicalReasoningPattern (Line 197)

Description: Logical Reasoning Pattern – Apply formal logic.

Handles logical operators, validates arguments, constructs proofs, and applies inference rules.

Methods:

`init` (Line 205)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

`register_agent` (Line 208)

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

Purpose: Register logical reasoning agent.

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

`run` (Line 212)

def run(self, scenarios):
    """Execute logical reasoning scenarios."""

Purpose: Execute logical reasoning scenarios.

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

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 = LogicalOperator(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: Spatial Reasoning


    """
Pattern 208: Spatial Reasoning Pattern

Reasons about spatial relationships, locations, distances, orientations,
and navigation. Handles concepts like left/right, near/far, inside/outside.
"""

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


class SpatialRelation(Enum):
    """Types of spatial relationships."""
    ABOVE = "above"
    BELOW = "below"
    LEFT_OF = "left_of"
    RIGHT_OF = "right_of"
    IN_FRONT_OF = "in_front_of"
    BEHIND = "behind"
    INSIDE = "inside"
    OUTSIDE = "outside"
    NEAR = "near"
    FAR = "far"
    TOUCHING = "touching"
    ADJACENT = "adjacent"


@dataclass
class SpatialObject:
    """Object with spatial properties."""
    name: str
    location: Tuple[float, float, float]  # x, y, z coordinates
    size: Tuple[float, float, float]  # width, height, depth
    orientation: float = 0.0  # degrees


@dataclass
class SpatialQuery:
    """Query about spatial relationships."""
    query_text: str
    objects_involved: List[str]
    query_type: str  # "relation", "path", "distance", "visibility"


class SpatialReasoningAgent:
    """Agent that performs spatial reasoning."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.objects: Dict[str, SpatialObject] = {}
    
    def add_object(self, obj: SpatialObject):
        """Add object to spatial environment."""
        self.objects[obj.name] = obj
    
    def determine_relation(self, obj1_name: str, obj2_name: str) -> str:
        """Determine spatial relation between two objects."""
        if obj1_name not in self.objects or obj2_name not in self.objects:
            return "unknown"
        
        obj1 = self.objects[obj1_name]
        obj2 = self.objects[obj2_name]
        
        dx = obj2.location[0] - obj1.location[0]
        dy = obj2.location[1] - obj1.location[1]
        dz = obj2.location[2] - obj1.location[2]
        
        # Simple spatial reasoning based on coordinates
        relations = []
        if dz > 0.5:
            relations.append("above")
        elif dz  0.5:
            relations.append("right of")
        elif dx  0.5:
            relations.append("in front of")
        elif dy < -0.5:
            relations.append("behind")
        
        distance = (dx**2 + dy**2 + dz**2) ** 0.5
        if distance  5.0:
            relations.append("far from")
        
        return ", ".join(relations) if relations else "at similar location"
    
    def calculate_distance(self, obj1_name: str, obj2_name: str) -> float:
        """Calculate Euclidean distance between objects."""
        if obj1_name not in self.objects or obj2_name not in self.objects:
            return -1.0
        
        obj1 = self.objects[obj1_name]
        obj2 = self.objects[obj2_name]
        
        dx = obj2.location[0] - obj1.location[0]
        dy = obj2.location[1] - obj1.location[1]
        dz = obj2.location[2] - obj1.location[2]
        
        return (dx**2 + dy**2 + dz**2) ** 0.5
    
    def find_path(self, start: str, goal: str, obstacles: List[str] = None) -> List[str]:
        """Find path from start to goal avoiding obstacles."""
        print(f"\n  {self.name}: Finding path from {start} to {goal}...")
        
        if start not in self.objects or goal not in self.objects:
            return []
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Plan a path from start to goal in 3D space."),
            ("human", """Start location: {start}
Goal location: {goal}
Obstacles: {obstacles}

Describe a path as a sequence of waypoints.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "start": str(self.objects[start].location),
            "goal": str(self.objects[goal].location),
            "obstacles": str([self.objects[o].location for o in (obstacles or [])])
        })
        
        waypoints = [line.strip() for line in response.content.split("\n") if line.strip()]
        print(f"  Path: {len(waypoints)} waypoints")
        return waypoints
    
    def check_visibility(self, observer: str, target: str) -> bool:
        """Check if target is visible from observer."""
        if observer not in self.objects or target not in self.objects:
            return False
        
        obs = self.objects[observer]
        tgt = self.objects[target]
        
        # Simple line-of-sight check (no obstacles for now)
        distance = self.calculate_distance(observer, target)
        return distance  Dict[str, Any]:
        """Reason about spatial scene from natural language."""
        print(f"\n  {self.name}: Reasoning about scene...")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Analyze spatial relationships in scene description."),
            ("human", """Scene: {scene}

Identify:
1. Objects and their locations
2. Spatial relationships between objects
3. Possible navigation paths

Provide structured analysis.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"scene": scene_description})
        
        return {
            "scene": scene_description,
            "analysis": response.content,
            "object_count": len(self.objects)
        }


class SpatialReasoningPattern:
    """
    Spatial Reasoning Pattern - Understand space and location.
    
    Handles spatial relationships, distances, paths, visibility,
    and scene understanding.
    """
    
    def __init__(self):
        self.agents: Dict[str, SpatialReasoningAgent] = {}
    
    def register_agent(self, agent: SpatialReasoningAgent):
        """Register spatial reasoning agent."""
        self.agents[agent.name] = agent
    
    def run(self, queries: List[SpatialQuery]) -> Dict[str, Any]:
        """Execute spatial reasoning queries."""
        print("=== Spatial Reasoning Pattern ===\n")
        
        for agent in self.agents.values():
            for query in queries:
                if query.query_type == "relation" and len(query.objects_involved) >= 2:
                    relation = agent.determine_relation(
                        query.objects_involved[0],
                        query.objects_involved[1]
                    )
                    print(f"\n  Relation: {query.objects_involved[0]} is {relation} {query.objects_involved[1]}")
                
                elif query.query_type == "distance" and len(query.objects_involved) >= 2:
                    distance = agent.calculate_distance(
                        query.objects_involved[0],
                        query.objects_involved[1]
                    )
                    print(f"\n  Distance: {distance:.2f} units")
                
                elif query.query_type == "path" and len(query.objects_involved) >= 2:
                    agent.find_path(
                        query.objects_involved[0],
                        query.objects_involved[1]
                    )
                
                elif query.query_type == "scene":
                    agent.reason_about_scene(query.query_text)
        
        return {
            "pattern": "SpatialReasoning",
            "agents": len(self.agents),
            "queries_processed": len(queries)
        }


# Example usage
if __name__ == "__main__":
    print("=== Spatial Reasoning Pattern Demo ===\n")
    
    # Create pattern and agent
    pattern = SpatialReasoningPattern()
    agent = SpatialReasoningAgent("Navigator")
    pattern.register_agent(agent)
    
    # Add objects to spatial environment
    print("1. Setting up spatial environment...\n")
    agent.add_object(SpatialObject("Table", (0.0, 0.0, 0.0), (2.0, 1.0, 0.8)))
    agent.add_object(SpatialObject("Chair", (-1.0, 0.0, 0.0), (0.5, 0.5, 1.0)))
    agent.add_object(SpatialObject("Lamp", (0.0, 0.0, 1.5), (0.2, 0.2, 0.5)))
    agent.add_object(SpatialObject("Door", (5.0, 0.0, 0.0), (1.0, 0.1, 2.0)))
    agent.add_object(SpatialObject("Window", (0.0, 3.0, 1.0), (1.5, 0.1, 1.5)))
    
    # Spatial reasoning queries
    print("2. Executing spatial reasoning queries...\n")
    queries = [
        SpatialQuery(
            "Where is the lamp relative to the table?",
            ["Lamp", "Table"],
            "relation"
        ),
        SpatialQuery(
            "How far is the door from the chair?",
            ["Door", "Chair"],
            "distance"
        ),
        SpatialQuery(
            "Find path from chair to door",
            ["Chair", "Door"],
            "path"
        ),
        SpatialQuery(
            "The room has a table in the center with a lamp on it. "
            "A chair is to the left, and a door is on the right wall.",
            [],
            "scene"
        )
    ]
    
    # Run spatial reasoning
    summary = pattern.run(queries)
    
    print(f"\n=== Summary ===")
    print(f"Queries processed: {summary['queries_processed']}")
    print(f"Objects in environment: {len(agent.objects)}")
    
    print("\n✓ Spatial reasoning: understanding locations and relationships!")

Agentic Architecture Pattern: Spatial Reasoning

Pattern 208: 208 Spatial Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 208: Spatial Reasoning Pattern

Reasons about spatial relationships, locations, distances, orientations, and navigation. Handles concepts like left/right, near/far, inside/outside.

Code Structure Analysis

File Information

Pattern Number: 208
Pattern Name: 208 Spatial Reasoning Pattern
File Path: patterns\advanced_reasoning\208_spatial_reasoning_pattern.py
Total Lines: 278

Detailed Code Explanation

Module Docstring

"""
Pattern 208: Spatial Reasoning Pattern

Reasons about spatial relationships, locations, distances, orientations,
and navigation. Handles concepts like left/right, near/far, inside/outside.
"""

Explanation: This module docstring describes the purpose and key features of the 208 Spatial Reasoning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass
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, Tuple: Type hints for better code documentation – from dataclasses import dataclass: Data class decorators for clean data structures – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Enumerations

SpatialRelation (Line 15)

class SpatialRelation(Enum):
    ABOVE
    BELOW
    LEFT_OF
    RIGHT_OF
    IN_FRONT_OF
    BEHIND
    INSIDE
    OUTSIDE
    NEAR
    FAR
    TOUCHING
    ADJACENT

Purpose: Types of spatial relationships.

Values: – ABOVE: Represents above – BELOW: Represents below – LEFT_OF: Represents left of – RIGHT_OF: Represents right of – IN_FRONT_OF: Represents in front of – BEHIND: Represents behind – INSIDE: Represents inside – OUTSIDE: Represents outside – NEAR: Represents near – FAR: Represents far – TOUCHING: Represents touching – ADJACENT: Represents adjacent

Classes

SpatialRelation (Line 15)

Description: Types of spatial relationships.

SpatialObject (Line 32)

Description: Object with spatial properties.

SpatialQuery (Line 41)

Description: Query about spatial relationships.

SpatialReasoningAgent (Line 48)

Description: Agent that performs spatial reasoning.

Methods:

`init` (Line 51)

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”)

`add_object` (Line 56)

def add_object(self, obj):
    """Add object to spatial environment."""

Purpose: Add object to spatial environment.

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

`determine_relation` (Line 60)

def determine_relation(self, obj1_name, obj2_name):
    """Determine spatial relation between two objects."""

Purpose: Determine spatial relation between two objects.

Parameters: – self: Instance reference – obj1_name: Parameter for obj1_name – obj2_name: Parameter for obj2_name

`calculate_distance` (Line 97)

def calculate_distance(self, obj1_name, obj2_name):
    """Calculate Euclidean distance between objects."""

Purpose: Calculate Euclidean distance between objects.

Parameters: – self: Instance reference – obj1_name: Parameter for obj1_name – obj2_name: Parameter for obj2_name

`find_path` (Line 111)

def find_path(self, start, goal, obstacles):
    """Find path from start to goal avoiding obstacles."""

Purpose: Find path from start to goal avoiding obstacles.

Parameters: – self: Instance reference – start: Parameter for start – goal: Parameter for goal – obstacles: Parameter for obstacles

`check_visibility` (Line 138)

def check_visibility(self, observer, target):
    """Check if target is visible from observer."""

Purpose: Check if target is visible from observer.

Parameters: – self: Instance reference – observer: Parameter for observer – target: Parameter for target

`reason_about_scene` (Line 150)

def reason_about_scene(self, scene_description):
    """Reason about spatial scene from natural language."""

Purpose: Reason about spatial scene from natural language.

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

SpatialReasoningPattern (Line 176)

Description: Spatial Reasoning Pattern – Understand space and location.

Handles spatial relationships, distances, paths, visibility, and scene understanding.

Methods:

`init` (Line 184)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

`register_agent` (Line 187)

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

Purpose: Register spatial reasoning agent.

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

`run` (Line 191)

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

Purpose: Execute spatial 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 = SpatialRelation(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: Temporal Reasoning


    """
Pattern 207: Temporal Reasoning Pattern

Reasons about time, temporal sequences, durations, and temporal constraints.
Handles before/after relationships, concurrency, and time-based planning.
"""

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


class TemporalRelation(Enum):
    """Allen's interval algebra relations."""
    BEFORE = "before"
    AFTER = "after"
    MEETS = "meets"
    OVERLAPS = "overlaps"
    DURING = "during"
    STARTS = "starts"
    FINISHES = "finishes"
    EQUALS = "equals"


@dataclass
class TemporalEvent:
    """Event with temporal information."""
    name: str
    start_time: Optional[datetime] = None
    end_time: Optional[datetime] = None
    duration: Optional[timedelta] = None
    description: str = ""


@dataclass
class TemporalConstraint:
    """Constraint between temporal events."""
    event1: str
    event2: str
    relation: TemporalRelation
    description: str = ""


class TemporalReasoningAgent:
    """Agent that performs temporal reasoning."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.events: List[TemporalEvent] = []
        self.constraints: List[TemporalConstraint] = []
    
    def add_event(self, event: TemporalEvent):
        """Add temporal event."""
        self.events.append(event)
    
    def infer_temporal_order(self, events_description: str) -> List[str]:
        """Infer temporal ordering of events from description."""
        print(f"\n  {self.name}: Inferring temporal order...")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Analyze text and determine temporal order of events."),
            ("human", """Text: {text}

List the events in chronological order (earliest to latest).
Format: one event per line.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"text": events_description})
        
        ordered_events = [line.strip().lstrip("0123456789.-) ") 
                         for line in response.content.split("\n") 
                         if line.strip()]
        
        print(f"  Ordered {len(ordered_events)} events")
        return ordered_events
    
    def calculate_duration(self, event_description: str) -> timedelta:
        """Estimate duration of an event."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Estimate typical duration in minutes for the event."),
            ("human", "Event: {event}\n\nTypical duration in minutes (just the number):")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"event": event_description})
        
        try:
            minutes = int(response.content.strip().split()[0])
            return timedelta(minutes=minutes)
        except:
            return timedelta(hours=1)  # Default
    
    def check_temporal_consistency(self) -> Dict[str, Any]:
        """Check if temporal constraints are consistent."""
        print(f"\n  {self.name}: Checking temporal consistency...")
        
        if len(self.events)  List[TemporalEvent]:
        """Plan schedule for tasks."""
        print(f"\n  {self.name}: Planning schedule for {len(tasks)} tasks...")
        
        schedule = []
        current_time = start_time
        
        for task in tasks:
            duration = self.calculate_duration(task)
            event = TemporalEvent(
                name=task,
                start_time=current_time,
                end_time=current_time + duration,
                duration=duration
            )
            schedule.append(event)
            self.events.append(event)
            current_time = event.end_time
            
            print(f"    {task}: {event.start_time.strftime('%H:%M')} - "
                  f"{event.end_time.strftime('%H:%M')} ({duration})")
        
        return schedule
    
    def reason_about_deadline(self, task: str, deadline: datetime,
                             current_time: datetime) -> Dict[str, Any]:
        """Reason about meeting a deadline."""
        time_remaining = deadline - current_time
        duration = self.calculate_duration(task)
        
        can_meet = time_remaining >= duration
        buffer = time_remaining - duration if can_meet else timedelta(0)
        
        return {
            "task": task,
            "deadline": deadline,
            "estimated_duration": duration,
            "time_remaining": time_remaining,
            "can_meet_deadline": can_meet,
            "buffer_time": buffer
        }


class TemporalReasoningPattern:
    """
    Temporal Reasoning Pattern - Reason about time.
    
    Handles temporal ordering, durations, constraints, scheduling,
    and deadline reasoning.
    """
    
    def __init__(self):
        self.agents: Dict[str, TemporalReasoningAgent] = {}
    
    def register_agent(self, agent: TemporalReasoningAgent):
        """Register temporal reasoning agent."""
        self.agents[agent.name] = agent
    
    def run(self, scenarios: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Execute temporal reasoning scenarios."""
        print("=== Temporal Reasoning Pattern ===\n")
        
        for agent in self.agents.values():
            for scenario in scenarios:
                scenario_type = scenario.get("type", "order")
                
                if scenario_type == "order":
                    agent.infer_temporal_order(scenario["text"])
                elif scenario_type == "schedule":
                    agent.plan_schedule(
                        scenario["tasks"],
                        scenario.get("start_time", datetime.now())
                    )
                elif scenario_type == "deadline":
                    result = agent.reason_about_deadline(
                        scenario["task"],
                        scenario["deadline"],
                        scenario.get("current_time", datetime.now())
                    )
                    print(f"\n  Deadline analysis:")
                    print(f"    Can meet: {result['can_meet_deadline']}")
                    print(f"    Buffer: {result['buffer_time']}")
        
        return {
            "pattern": "TemporalReasoning",
            "agents": len(self.agents),
            "scenarios_processed": len(scenarios),
            "events_tracked": sum(len(a.events) for a in self.agents.values())
        }


# Example usage
if __name__ == "__main__":
    print("=== Temporal Reasoning Pattern Demo ===\n")
    
    # Create pattern and agent
    pattern = TemporalReasoningPattern()
    agent = TemporalReasoningAgent("TimeKeeper")
    pattern.register_agent(agent)
    
    # Temporal reasoning scenarios
    print("1. Temporal ordering scenario...")
    scenarios = [
        {
            "type": "order",
            "text": "First, I woke up. Then I had breakfast. After that, I went to work. "
                   "During lunch, I met a friend. Finally, I came home in the evening."
        },
        {
            "type": "schedule",
            "tasks": ["Morning meeting", "Code review", "Lunch break", "Development work"],
            "start_time": datetime.now().replace(hour=9, minute=0, second=0, microsecond=0)
        },
        {
            "type": "deadline",
            "task": "Write project report",
            "deadline": datetime.now() + timedelta(hours=4),
            "current_time": datetime.now()
        }
    ]
    
    # Run temporal reasoning
    summary = pattern.run(scenarios)
    
    print(f"\n=== Summary ===")
    print(f"Scenarios processed: {summary['scenarios_processed']}")
    print(f"Events tracked: {summary['events_tracked']}")
    
    print("\n✓ Temporal reasoning: understanding time and sequences!")

Agentic Architecture Pattern: Temporal Reasoning

Pattern 207: 207 Temporal Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 207: Temporal Reasoning Pattern

Reasons about time, temporal sequences, durations, and temporal constraints. Handles before/after relationships, concurrency, and time-based planning.

Code Structure Analysis

File Information

Pattern Number: 207
Pattern Name: 207 Temporal Reasoning Pattern
File Path: patterns\advanced_reasoning\207_temporal_reasoning_pattern.py
Total Lines: 258

Detailed Code Explanation

Module Docstring

"""
Pattern 207: Temporal Reasoning Pattern

Reasons about time, temporal sequences, durations, and temporal constraints.
Handles before/after relationships, concurrency, and time-based planning.
"""

Explanation: This module docstring describes the purpose and key features of the 207 Temporal Reasoning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI  # 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, timedelta: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Enumerations

TemporalRelation (Line 16)

class TemporalRelation(Enum):
    BEFORE
    AFTER
    MEETS
    OVERLAPS
    DURING
    STARTS
    FINISHES
    EQUALS

Purpose: Allen’s interval algebra relations.

Values: – BEFORE: Represents before – AFTER: Represents after – MEETS: Represents meets – OVERLAPS: Represents overlaps – DURING: Represents during – STARTS: Represents starts – FINISHES: Represents finishes – EQUALS: Represents equals

Classes

TemporalRelation (Line 16)

Description: Allen’s interval algebra relations.

TemporalEvent (Line 29)

Description: Event with temporal information.

TemporalConstraint (Line 39)

Description: Constraint between temporal events.

TemporalReasoningAgent (Line 47)

Description: Agent that performs temporal reasoning.

Methods:

`init` (Line 50)

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”)

`add_event` (Line 56)

def add_event(self, event):
    """Add temporal event."""

Purpose: Add temporal event.

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

`infer_temporal_order` (Line 60)

def infer_temporal_order(self, events_description):
    """Infer temporal ordering of events from description."""

Purpose: Infer temporal ordering of events from description.

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

`calculate_duration` (Line 82)

def calculate_duration(self, event_description):
    """Estimate duration of an event."""

Purpose: Estimate duration of an event.

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

`check_temporal_consistency` (Line 98)

def check_temporal_consistency(self):
    """Check if temporal constraints are consistent."""

Purpose: Check if temporal constraints are consistent.

Parameters: – self: Instance reference

`plan_schedule` (Line 128)

def plan_schedule(self, tasks, start_time):
    """Plan schedule for tasks."""

Purpose: Plan schedule for tasks.

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

`reason_about_deadline` (Line 153)

def reason_about_deadline(self, task, deadline, current_time):
    """Reason about meeting a deadline."""

Purpose: Reason about meeting a deadline.

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

TemporalReasoningPattern (Line 172)

Description: Temporal Reasoning Pattern – Reason about time.

Handles temporal ordering, durations, constraints, scheduling, and deadline reasoning.

Methods:

`init` (Line 180)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

`register_agent` (Line 183)

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

Purpose: Register temporal reasoning agent.

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

`run` (Line 187)

def run(self, scenarios):
    """Execute temporal reasoning scenarios."""

Purpose: Execute temporal reasoning scenarios.

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

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 = TemporalRelation(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: Causal Reasoning


    """
Pattern 206: Causal Reasoning Pattern

Identifies cause-and-effect relationships, understands causal chains,
and predicts outcomes based on causal mechanisms.
"""

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 CausalRelationType(Enum):
    """Types of causal relationships."""
    DIRECT = "direct"  # A directly causes B
    INDIRECT = "indirect"  # A causes B through intermediary C
    CONTRIBUTORY = "contributory"  # A contributes to B
    PREVENTIVE = "preventive"  # A prevents B
    NECESSARY = "necessary"  # A is necessary for B
    SUFFICIENT = "sufficient"  # A is sufficient for B


@dataclass
class CausalRelation:
    """Represents a cause-effect relationship."""
    cause: str
    effect: str
    relation_type: CausalRelationType
    strength: float = 0.8  # 0.0 to 1.0
    mechanism: str = ""  # How cause leads to effect
    intermediaries: List[str] = field(default_factory=list)


@dataclass
class CausalQuery:
    """Query about causal relationships."""
    question: str
    context: Dict[str, Any]
    query_type: str  # "cause", "effect", "mechanism", "intervention"


class CausalReasoningAgent:
    """Agent that performs causal reasoning."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.causal_graph: List[CausalRelation] = []
        self.interventions: List[Dict[str, Any]] = []
    
    def identify_cause(self, effect: str, context: Dict[str, Any]) -> List[str]:
        """Identify possible causes of an effect."""
        print(f"\n  {self.name}: Identifying causes of '{effect}'...")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a causal reasoning expert. Identify likely causes."),
            ("human", """Effect: {effect}
Context: {context}

What are the most likely causes? List 2-3 primary causes, one per line.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"effect": effect, "context": str(context)})
        
        causes = [line.strip().lstrip("0123456789.-) ") 
                 for line in response.content.split("\n") 
                 if line.strip()]
        
        print(f"  Identified {len(causes)} potential causes")
        return causes[:3]
    
    def predict_effect(self, cause: str, context: Dict[str, Any]) -> List[str]:
        """Predict effects of a cause."""
        print(f"\n  {self.name}: Predicting effects of '{cause}'...")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a causal reasoning expert. Predict likely effects."),
            ("human", """Cause: {cause}
Context: {context}

What effects will this cause? List 2-3 primary effects, one per line.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"cause": cause, "context": str(context)})
        
        effects = [line.strip().lstrip("0123456789.-) ") 
                  for line in response.content.split("\n") 
                  if line.strip()]
        
        print(f"  Predicted {len(effects)} effects")
        return effects[:3]
    
    def explain_mechanism(self, cause: str, effect: str) -> str:
        """Explain causal mechanism linking cause to effect."""
        print(f"\n  {self.name}: Explaining mechanism: {cause} → {effect}")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Explain the causal mechanism - how does the cause lead to the effect?"),
            ("human", "Cause: {cause}\nEffect: {effect}\n\nExplain the causal mechanism:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"cause": cause, "effect": effect})
        
        mechanism = response.content.strip()
        print(f"  Mechanism: {mechanism[:80]}...")
        return mechanism
    
    def build_causal_chain(self, initial_cause: str, 
                          context: Dict[str, Any]) -> List[CausalRelation]:
        """Build a causal chain from initial cause."""
        print(f"\n  {self.name}: Building causal chain from '{initial_cause}'...")
        
        chain = []
        current_cause = initial_cause
        
        for step in range(3):  # Max 3 steps
            effects = self.predict_effect(current_cause, context)
            if not effects:
                break
            
            primary_effect = effects[0]
            mechanism = self.explain_mechanism(current_cause, primary_effect)
            
            relation = CausalRelation(
                cause=current_cause,
                effect=primary_effect,
                relation_type=CausalRelationType.DIRECT,
                mechanism=mechanism,
                strength=0.8 - (step * 0.1)  # Strength decreases with distance
            )
            
            chain.append(relation)
            self.causal_graph.append(relation)
            current_cause = primary_effect
        
        print(f"  Built chain with {len(chain)} causal links")
        return chain
    
    def simulate_intervention(self, intervention: str, 
                            target_effect: str) -> Dict[str, Any]:
        """Simulate effect of intervention."""
        print(f"\n  {self.name}: Simulating intervention: {intervention}")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Analyze causal impact of intervention."),
            ("human", """Intervention: {intervention}
Target Effect: {target_effect}

Will this intervention affect the target effect? How?
Provide: IMPACT (increase/decrease/no_change) and CONFIDENCE (0.0-1.0)""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "intervention": intervention,
            "target_effect": target_effect
        })
        
        result = {
            "intervention": intervention,
            "target_effect": target_effect,
            "prediction": response.content,
            "confidence": 0.7
        }
        
        self.interventions.append(result)
        return result


class CausalReasoningPattern:
    """
    Causal Reasoning Pattern - Understand cause and effect.
    
    Identifies causes, predicts effects, explains mechanisms,
    builds causal chains, and simulates interventions.
    """
    
    def __init__(self):
        self.agents: Dict[str, CausalReasoningAgent] = {}
    
    def register_agent(self, agent: CausalReasoningAgent):
        """Register causal reasoning agent."""
        self.agents[agent.name] = agent
    
    def run(self, queries: List[CausalQuery]) -> Dict[str, Any]:
        """Execute causal reasoning on queries."""
        print("=== Causal Reasoning Pattern ===\n")
        
        for agent in self.agents.values():
            for query in queries:
                context = query.context
                
                if query.query_type == "cause":
                    agent.identify_cause(query.question, context)
                elif query.query_type == "effect":
                    agent.predict_effect(query.question, context)
                elif query.query_type == "chain":
                    agent.build_causal_chain(query.question, context)
                elif query.query_type == "intervention":
                    agent.simulate_intervention(
                        query.question, 
                        context.get("target_effect", "unknown")
                    )
        
        return {
            "pattern": "CausalReasoning",
            "agents": len(self.agents),
            "queries_processed": len(queries),
            "causal_relations": sum(len(a.causal_graph) for a in self.agents.values())
        }


# Example usage
if __name__ == "__main__":
    print("=== Causal Reasoning Pattern Demo ===\n")
    
    # Create pattern and agent
    pattern = CausalReasoningPattern()
    agent = CausalReasoningAgent("CausalAnalyst")
    pattern.register_agent(agent)
    
    # Causal reasoning queries
    queries = [
        CausalQuery(
            question="Global temperature increase",
            context={"domain": "climate"},
            query_type="cause"
        ),
        CausalQuery(
            question="Increased carbon emissions",
            context={"domain": "climate"},
            query_type="effect"
        ),
        CausalQuery(
            question="Poor sleep quality",
            context={"person": "adult", "lifestyle": "busy"},
            query_type="chain"
        ),
        CausalQuery(
            question="Regular exercise routine",
            context={"target_effect": "improved health"},
            query_type="intervention"
        ),
    ]
    
    # Run causal reasoning
    summary = pattern.run(queries)
    
    print(f"\n=== Summary ===")
    print(f"Queries processed: {summary['queries_processed']}")
    print(f"Causal relations identified: {summary['causal_relations']}")
    
    print(f"\n=== Causal Graph ===")
    for relation in agent.causal_graph:
        print(f"{relation.cause} → {relation.effect}")
        print(f"  Type: {relation.relation_type.value}, Strength: {relation.strength}")
    
    print("\n✓ Causal reasoning: understanding cause and effect!")

Agentic Architecture Pattern: Causal Reasoning

Pattern 206: 206 Causal Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 206: Causal Reasoning Pattern

Identifies cause-and-effect relationships, understands causal chains, and predicts outcomes based on causal mechanisms.

Code Structure Analysis

File Information

Pattern Number: 206
Pattern Name: 206 Causal Reasoning Pattern
File Path: patterns\advanced_reasoning\206_causal_reasoning_pattern.py
Total Lines: 265

Detailed Code Explanation

Module Docstring

"""
Pattern 206: Causal Reasoning Pattern

Identifies cause-and-effect relationships, understands causal chains,
and predicts outcomes based on causal mechanisms.
"""

Explanation: This module docstring describes the purpose and key features of the 206 Causal 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

CausalRelationType (Line 16)

class CausalRelationType(Enum):
    DIRECT
    INDIRECT
    CONTRIBUTORY
    PREVENTIVE
    NECESSARY
    SUFFICIENT

Purpose: Types of causal relationships.

Values: – DIRECT: Represents direct – INDIRECT: Represents indirect – CONTRIBUTORY: Represents contributory – PREVENTIVE: Represents preventive – NECESSARY: Represents necessary – SUFFICIENT: Represents sufficient

Classes

CausalRelationType (Line 16)

Description: Types of causal relationships.

CausalRelation (Line 27)

Description: Represents a cause-effect relationship.

CausalQuery (Line 38)

Description: Query about causal relationships.

CausalReasoningAgent (Line 45)

Description: Agent that performs causal reasoning.

Methods:

`init` (Line 48)

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”)

`identify_cause` (Line 54)

def identify_cause(self, effect, context):
    """Identify possible causes of an effect."""

Purpose: Identify possible causes of an effect.

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

`predict_effect` (Line 76)

def predict_effect(self, cause, context):
    """Predict effects of a cause."""

Purpose: Predict effects of a cause.

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

`explain_mechanism` (Line 98)

def explain_mechanism(self, cause, effect):
    """Explain causal mechanism linking cause to effect."""

Purpose: Explain causal mechanism linking cause to effect.

Parameters: – self: Instance reference – cause: Parameter for cause – effect: Parameter for effect

`build_causal_chain` (Line 114)

def build_causal_chain(self, initial_cause, context):
    """Build a causal chain from initial cause."""

Purpose: Build a causal chain from initial cause.

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

`simulate_intervention` (Line 145)

def simulate_intervention(self, intervention, target_effect):
    """Simulate effect of intervention."""

Purpose: Simulate effect of intervention.

Parameters: – self: Instance reference – intervention: Parameter for intervention – target_effect: Parameter for target_effect

CausalReasoningPattern (Line 176)

Description: Causal Reasoning Pattern – Understand cause and effect.

Identifies causes, predicts effects, explains mechanisms, builds causal chains, and simulates interventions.

Methods:

`init` (Line 184)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

`register_agent` (Line 187)

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

Purpose: Register causal reasoning agent.

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

`run` (Line 191)

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

Purpose: Execute causal reasoning on 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 = CausalRelationType(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: Commonsense Reasoning


    """
Pattern 205: Commonsense Reasoning Pattern

Applies everyday knowledge and intuitive understanding to reason about
situations. Handles implicit knowledge that humans take for granted.
"""

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 KnowledgeType(Enum):
    """Types of commonsense knowledge."""
    PHYSICAL = "physical"  # Gravity, objects fall
    SOCIAL = "social"  # People have feelings
    TEMPORAL = "temporal"  # Time flows forward
    SPATIAL = "spatial"  # Objects have location
    CAUSAL = "causal"  # Actions have consequences
    FUNCTIONAL = "functional"  # Objects have purposes


@dataclass
class CommonsenseKnowledge:
    """Piece of commonsense knowledge."""
    statement: str
    knowledge_type: KnowledgeType
    confidence: float = 0.9
    source: str = "general"


@dataclass
class ReasoningQuery:
    """Query requiring commonsense reasoning."""
    question: str
    context: Dict[str, Any]
    requires_knowledge: List[KnowledgeType] = field(default_factory=list)


@dataclass
class CommonsenseAnswer:
    """Answer based on commonsense reasoning."""
    query: str
    answer: str
    reasoning: str
    knowledge_used: List[CommonsenseKnowledge]
    confidence: float


class CommonsenseKnowledgeBase:
    """Storage for commonsense knowledge."""
    
    def __init__(self):
        self.knowledge: List[CommonsenseKnowledge] = []
        self._populate_basic_knowledge()
    
    def _populate_basic_knowledge(self):
        """Populate with basic commonsense knowledge."""
        basic_knowledge = [
            CommonsenseKnowledge("Objects fall down due to gravity", KnowledgeType.PHYSICAL),
            CommonsenseKnowledge("People need food and water to survive", KnowledgeType.PHYSICAL),
            CommonsenseKnowledge("Time moves forward, not backward", KnowledgeType.TEMPORAL),
            CommonsenseKnowledge("People feel pain when hurt", KnowledgeType.SOCIAL),
            CommonsenseKnowledge("Tools are used to accomplish tasks", KnowledgeType.FUNCTIONAL),
            CommonsenseKnowledge("Actions have consequences", KnowledgeType.CAUSAL),
        ]
        self.knowledge.extend(basic_knowledge)
    
    def get_relevant_knowledge(self, knowledge_types: List[KnowledgeType]) -> List[CommonsenseKnowledge]:
        """Get knowledge relevant to types."""
        if not knowledge_types:
            return self.knowledge
        return [k for k in self.knowledge if k.knowledge_type in knowledge_types]
    
    def add_knowledge(self, knowledge: CommonsenseKnowledge):
        """Add new commonsense knowledge."""
        self.knowledge.append(knowledge)


class CommonsenseReasoningAgent:
    """Agent that applies commonsense reasoning."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.5)
        self.knowledge_base = CommonsenseKnowledgeBase()
        self.answers: List[CommonsenseAnswer] = []
    
    def reason(self, query: ReasoningQuery) -> CommonsenseAnswer:
        """Apply commonsense reasoning to query."""
        print(f"\n  {self.name}: Reasoning about: {query.question[:60]}...")
        
        # Get relevant knowledge
        relevant_knowledge = self.knowledge_base.get_relevant_knowledge(
            query.requires_knowledge
        )
        
        knowledge_text = "\n".join([
            f"- {k.statement} ({k.knowledge_type.value})"
            for k in relevant_knowledge
        ])
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are applying commonsense reasoning. Use everyday knowledge "
                      "that people typically understand intuitively."),
            ("human", """Question: {question}

Context: {context}

Relevant Commonsense Knowledge:
{knowledge}

Provide:
1. A direct answer
2. Step-by-step reasoning showing commonsense logic
3. Confidence level (0.0-1.0)

Format:
ANSWER: 
REASONING: 
CONFIDENCE: """)
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "question": query.question,
            "context": str(query.context),
            "knowledge": knowledge_text
        })
        
        # Parse response
        answer_text = ""
        reasoning_text = ""
        confidence = 0.8
        
        for line in response.content.split("\n"):
            if line.startswith("ANSWER:"):
                answer_text = line.replace("ANSWER:", "").strip()
            elif line.startswith("REASONING:"):
                reasoning_text = line.replace("REASONING:", "").strip()
            elif line.startswith("CONFIDENCE:"):
                try:
                    confidence = float(line.replace("CONFIDENCE:", "").strip())
                except:
                    confidence = 0.8
        
        if not answer_text:
            answer_text = response.content[:200]
        if not reasoning_text:
            reasoning_text = "Applied commonsense reasoning"
        
        answer = CommonsenseAnswer(
            query=query.question,
            answer=answer_text,
            reasoning=reasoning_text,
            knowledge_used=relevant_knowledge,
            confidence=confidence
        )
        
        self.answers.append(answer)
        print(f"  Answer: {answer_text[:80]}...")
        print(f"  Confidence: {confidence}")
        
        return answer


class CommonsenseReasoningPattern:
    """
    Commonsense Reasoning Pattern - Apply everyday knowledge.
    
    Uses intuitive, everyday knowledge to reason about situations
    that require implicit understanding.
    """
    
    def __init__(self):
        self.agents: Dict[str, CommonsenseReasoningAgent] = {}
    
    def register_agent(self, agent: CommonsenseReasoningAgent):
        """Register commonsense reasoning agent."""
        self.agents[agent.name] = agent
    
    def run(self, queries: List[ReasoningQuery]) -> Dict[str, Any]:
        """Execute commonsense reasoning on queries."""
        print("=== Commonsense Reasoning Pattern ===\n")
        
        for agent in self.agents.values():
            print(f"{agent.name}: Processing {len(queries)} queries...")
            for query in queries:
                agent.reason(query)
        
        return {
            "pattern": "CommonsenseReasoning",
            "agents": len(self.agents),
            "queries_processed": len(queries),
            "total_answers": sum(len(a.answers) for a in self.agents.values())
        }


# Example usage
if __name__ == "__main__":
    print("=== Commonsense Reasoning Pattern Demo ===\n")
    
    # Create pattern and agent
    pattern = CommonsenseReasoningPattern()
    agent = CommonsenseReasoningAgent("CommonSenseBot")
    pattern.register_agent(agent)
    
    # Queries requiring commonsense
    print("1. Creating queries requiring commonsense reasoning...\n")
    queries = [
        ReasoningQuery(
            question="If I drop a glass on a hard floor, what will likely happen?",
            context={"object": "glass", "surface": "hard floor", "action": "drop"},
            requires_knowledge=[KnowledgeType.PHYSICAL, KnowledgeType.CAUSAL]
        ),
        ReasoningQuery(
            question="If someone hasn't eaten all day, what should I offer them?",
            context={"person": "hungry", "time_without_food": "all day"},
            requires_knowledge=[KnowledgeType.PHYSICAL, KnowledgeType.SOCIAL]
        ),
        ReasoningQuery(
            question="Why would someone use an umbrella on a rainy day?",
            context={"weather": "rainy", "object": "umbrella"},
            requires_knowledge=[KnowledgeType.FUNCTIONAL, KnowledgeType.PHYSICAL]
        ),
        ReasoningQuery(
            question="If I'm late for an important meeting, how should I communicate?",
            context={"situation": "late", "event": "important meeting"},
            requires_knowledge=[KnowledgeType.SOCIAL, KnowledgeType.TEMPORAL]
        ),
    ]
    
    # Run reasoning
    summary = pattern.run(queries)
    
    print(f"\n2. Summary:")
    print(f"   Queries processed: {summary['queries_processed']}")
    print(f"   Total answers: {summary['total_answers']}")
    
    print(f"\n3. Answers with Reasoning:")
    for i, answer in enumerate(agent.answers, 1):
        print(f"\n   Query {i}: {answer.query[:50]}...")
        print(f"   Answer: {answer.answer}")
        print(f"   Reasoning: {answer.reasoning[:100]}...")
        print(f"   Knowledge types used: {len(answer.knowledge_used)}")
    
    print("\n✓ Commonsense reasoning applied everyday knowledge!")

Agentic Architecture Pattern: Commonsense Reasoning

Pattern 205: 205 Commonsense Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 205: Commonsense Reasoning Pattern

Applies everyday knowledge and intuitive understanding to reason about situations. Handles implicit knowledge that humans take for granted.

Code Structure Analysis

File Information

Pattern Number: 205
Pattern Name: 205 Commonsense Reasoning Pattern
File Path: patterns\advanced_reasoning\205_commonsense_reasoning_pattern.py
Total Lines: 251

Detailed Code Explanation

Module Docstring

"""
Pattern 205: Commonsense Reasoning Pattern

Applies everyday knowledge and intuitive understanding to reason about
situations. Handles implicit knowledge that humans take for granted.
"""

Explanation: This module docstring describes the purpose and key features of the 205 Commonsense 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

KnowledgeType (Line 16)

class KnowledgeType(Enum):
    PHYSICAL
    SOCIAL
    TEMPORAL
    SPATIAL
    CAUSAL
    FUNCTIONAL

Purpose: Types of commonsense knowledge.

Values: – PHYSICAL: Represents physical – SOCIAL: Represents social – TEMPORAL: Represents temporal – SPATIAL: Represents spatial – CAUSAL: Represents causal – FUNCTIONAL: Represents functional

Classes

KnowledgeType (Line 16)

Description: Types of commonsense knowledge.

CommonsenseKnowledge (Line 27)

Description: Piece of commonsense knowledge.

ReasoningQuery (Line 36)

Description: Query requiring commonsense reasoning.

CommonsenseAnswer (Line 44)

Description: Answer based on commonsense reasoning.

CommonsenseKnowledgeBase (Line 53)

Description: Storage for commonsense knowledge.

Methods:

`init` (Line 56)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

`_populate_basic_knowledge` (Line 60)

def _populate_basic_knowledge(self):
    """Populate with basic commonsense knowledge."""

Purpose: Populate with basic commonsense knowledge.

Parameters: – self: Instance reference

`get_relevant_knowledge` (Line 72)

def get_relevant_knowledge(self, knowledge_types):
    """Get knowledge relevant to types."""

Purpose: Get knowledge relevant to types.

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

`add_knowledge` (Line 78)

def add_knowledge(self, knowledge):
    """Add new commonsense knowledge."""

Purpose: Add new commonsense knowledge.

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

CommonsenseReasoningAgent (Line 83)

Description: Agent that applies commonsense reasoning.

Methods:

`init` (Line 86)

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”)

`reason` (Line 92)

def reason(self, query):
    """Apply commonsense reasoning to query."""

Purpose: Apply commonsense reasoning to query.

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

CommonsenseReasoningPattern (Line 170)

Description: Commonsense Reasoning Pattern – Apply everyday knowledge.

Uses intuitive, everyday knowledge to reason about situations that require implicit understanding.

Methods:

`init` (Line 178)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

`register_agent` (Line 181)

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

Purpose: Register commonsense reasoning agent.

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

`run` (Line 185)

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

Purpose: Execute commonsense reasoning on 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 = KnowledgeType(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: Inductive Reasoning


    """
Pattern 204: Inductive Reasoning Pattern

Reasons from specific observations to general principles through pattern
recognition and generalization. Infers general rules from examples.
"""

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


@dataclass
class Observation:
    """Single observation/example."""
    description: str
    attributes: Dict[str, Any]
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class GeneralPrinciple:
    """Inferred general principle."""
    principle: str
    confidence: float  # 0.0 to 1.0
    supporting_observations: List[Observation]
    counter_examples: List[Observation] = field(default_factory=list)


class InductiveReasoningAgent:
    """Agent that performs inductive reasoning."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.observations: List[Observation] = []
        self.principles: List[GeneralPrinciple] = []
    
    def add_observation(self, observation: Observation):
        """Add new observation."""
        self.observations.append(observation)
        print(f"  {self.name}: Added observation - {observation.description}")
    
    def induce_principles(self) -> List[GeneralPrinciple]:
        """Induce general principles from observations."""
        print(f"\n  {self.name}: Inducing principles from {len(self.observations)} observations...")
        
        if len(self.observations)  2 else ""
                    
                    # Parse supporting observation indices
                    supporting_obs = []
                    for idx_str in indices_str.split(","):
                        try:
                            idx = int(idx_str.strip()) - 1
                            if 0 <= idx  bool:
        """Test if principle applies to new observation."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Determine if a general principle applies to a new observation."),
            ("human", """General Principle: {principle}

New Observation: {observation}
Attributes: {attributes}

Does this principle apply? Answer YES or NO with brief explanation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "principle": principle.principle,
            "observation": test_observation.description,
            "attributes": str(test_observation.attributes)
        })
        
        result = "YES" in response.content.upper()
        if result:
            principle.supporting_observations.append(test_observation)
        else:
            principle.counter_examples.append(test_observation)
        
        return result


class InductiveReasoningPattern:
    """
    Inductive Reasoning Pattern - From specific to general.
    
    Collects observations, identifies patterns, induces general principles,
    and tests them against new observations.
    """
    
    def __init__(self):
        self.agents: Dict[str, InductiveReasoningAgent] = {}
    
    def register_agent(self, agent: InductiveReasoningAgent):
        """Register inductive reasoning agent."""
        self.agents[agent.name] = agent
    
    def run(self, observations: List[Observation], 
            test_cases: Optional[List[Observation]] = None) -> Dict[str, Any]:
        """Execute inductive reasoning process."""
        print("=== Inductive Reasoning Pattern ===\n")
        
        for agent in self.agents.values():
            # Add observations
            print(f"{agent.name}: Collecting observations...")
            for obs in observations:
                agent.add_observation(obs)
            
            # Induce principles
            principles = agent.induce_principles()
            
            # Test principles if test cases provided
            if test_cases:
                print(f"\n  Testing principles with new observations...")
                for test_obs in test_cases:
                    print(f"\n  Test: {test_obs.description}")
                    for i, principle in enumerate(principles, 1):
                        result = agent.test_principle(principle, test_obs)
                        print(f"    Principle {i}: {'✓ Applies' if result else '✗ Does not apply'}")
        
        return {
            "pattern": "InductiveReasoning",
            "agents": len(self.agents),
            "total_observations": sum(len(a.observations) for a in self.agents.values()),
            "total_principles": sum(len(a.principles) for a in self.agents.values())
        }


# Example usage
if __name__ == "__main__":
    print("=== Inductive Reasoning Pattern Demo ===\n")
    
    # Create pattern and agent
    pattern = InductiveReasoningPattern()
    agent = InductiveReasoningAgent("Scientist")
    pattern.register_agent(agent)
    
    # Training observations (specific examples)
    print("1. Collecting specific observations...\n")
    observations = [
        Observation("The swan in the park is white", {"color": "white", "animal": "swan"}),
        Observation("Another swan at the lake is white", {"color": "white", "animal": "swan"}),
        Observation("The swan at the zoo is also white", {"color": "white", "animal": "swan"}),
        Observation("Swans in the book pictures are white", {"color": "white", "animal": "swan"}),
        Observation("Heated metal expands", {"temperature": "hot", "material": "metal", "state": "expanded"}),
        Observation("Heated water expands", {"temperature": "hot", "material": "water", "state": "expanded"}),
        Observation("Heated air expands", {"temperature": "hot", "material": "air", "state": "expanded"}),
    ]
    
    # Test cases (new observations)
    test_cases = [
        Observation("A black swan in Australia", {"color": "black", "animal": "swan"}),
        Observation("Heated plastic expands", {"temperature": "hot", "material": "plastic", "state": "expanded"}),
    ]
    
    # Run inductive reasoning
    summary = pattern.run(observations, test_cases)
    
    print(f"\n2. Summary:")
    print(f"   Total observations: {summary['total_observations']}")
    print(f"   Principles induced: {summary['total_principles']}")
    
    print(f"\n3. Induced Principles:")
    for i, principle in enumerate(agent.principles, 1):
        print(f"   {i}. {principle.principle}")
        print(f"      Confidence: {principle.confidence}")
        print(f"      Supported by: {len(principle.supporting_observations)} observations")
        print(f"      Counter-examples: {len(principle.counter_examples)}")
    
    print("\n✓ Inductive reasoning: specific observations → general principles!")

Agentic Architecture Pattern: Inductive Reasoning

Pattern 204: 204 Inductive Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 204: Inductive Reasoning Pattern

Reasons from specific observations to general principles through pattern recognition and generalization. Infers general rules from examples.

Code Structure Analysis

File Information

Pattern Number: 204
Pattern Name: 204 Inductive Reasoning Pattern
File Path: patterns\advanced_reasoning\204_inductive_reasoning_pattern.py
Total Lines: 231

Detailed Code Explanation

Module Docstring

"""
Pattern 204: Inductive Reasoning Pattern

Reasons from specific observations to general principles through pattern
recognition and generalization. Infers general rules from examples.
"""

Explanation: This module docstring describes the purpose and key features of the 204 Inductive Reasoning Pattern pattern.

Imports Section

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

Classes

Observation (Line 16)

Description: Single observation/example.

GeneralPrinciple (Line 24)

Description: Inferred general principle.

InductiveReasoningAgent (Line 32)

Description: Agent that performs inductive reasoning.

Methods:

`init` (Line 35)

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”)

`add_observation` (Line 41)

def add_observation(self, observation):
    """Add new observation."""

Purpose: Add new observation.

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

`induce_principles` (Line 46)

def induce_principles(self):
    """Induce general principles from observations."""

Purpose: Induce general principles from observations.

Parameters: – self: Instance reference

`test_principle` (Line 114)

def test_principle(self, principle, test_observation):
    """Test if principle applies to new observation."""

Purpose: Test if principle applies to new observation.

Parameters: – self: Instance reference – principle: Parameter for principle – test_observation: Parameter for test_observation

InductiveReasoningPattern (Line 143)

Description: Inductive Reasoning Pattern – From specific to general.

Collects observations, identifies patterns, induces general principles, and tests them against new observations.

Methods:

`init` (Line 151)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

`register_agent` (Line 154)

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

Purpose: Register inductive reasoning agent.

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

`run` (Line 158)

def run(self, observations, test_cases):
    """Execute inductive reasoning process."""

Purpose: Execute inductive reasoning process.

Parameters: – self: Instance reference – observations: Parameter for observations – test_cases: Parameter for test_cases

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 = Observation(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: Deductive Reasoning


    """
Pattern 203: Deductive Reasoning Pattern

Applies general rules to specific cases to draw logical conclusions.
If premises are true, conclusion must be true (valid deduction).
"""

from typing import Dict, Any, List
from dataclasses import dataclass
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI


@dataclass
class Premise:
    """A premise in deductive reasoning."""
    statement: str
    premise_type: str = "general"  # general, specific, conditional


@dataclass
class DeductiveArgument:
    """A deductive argument."""
    premises: List[Premise]
    conclusion: str
    is_valid: bool = False
    explanation: str = ""


class DeductiveReasoningPattern:
    """
    Deductive Reasoning Pattern - Logical inference from premises.
    
    Applies formal logic: given premises, deduce valid conclusions.
    Checks validity of arguments.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.arguments: List[DeductiveArgument] = []
    
    def apply_modus_ponens(self, conditional: str, antecedent: str) -> str:
        """Apply modus ponens: If P then Q, P is true, therefore Q."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Apply modus ponens deduction: If P then Q, and P is true, then Q must be true."),
            ("human", """Conditional: {conditional}
Antecedent: {antecedent}

What can we deduce? Provide the conclusion:""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"conditional": conditional, "antecedent": antecedent})
        return response.content
    
    def validate_argument(self, premises: List[str], conclusion: str) -> DeductiveArgument:
        """Validate a deductive argument."""
        print(f"\n=== Validating Deductive Argument ===")
        print(f"Premises: {len(premises)}")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Analyze if the conclusion logically follows from the premises. "
                      "Check for validity using formal logic rules."),
            ("human", """Premises:
{premises}

Proposed Conclusion: {conclusion}

Is this argument valid? Respond: VALID or INVALID|explanation""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "premises": "\n".join([f"{i+1}. {p}" for i, p in enumerate(premises)]),
            "conclusion": conclusion
        })
        
        # Parse validity
        content = response.content.strip()
        is_valid = content.startswith("VALID")
        
        parts = content.split('|')
        explanation = parts[1].strip() if len(parts) > 1 else content
        
        argument = DeductiveArgument(
            premises=[Premise(p) for p in premises],
            conclusion=conclusion,
            is_valid=is_valid,
            explanation=explanation
        )
        
        self.arguments.append(argument)
        
        print(f"Validity: {'VALID' if is_valid else 'INVALID'}")
        return argument
    
    def run(self, premises: List[str], conclusion: str) -> Dict[str, Any]:
        """Perform deductive reasoning."""
        print(f"\n=== Deductive Reasoning Pattern ===")
        
        argument = self.validate_argument(premises, conclusion)
        
        return {
            "pattern": "DeductiveReasoning",
            "premises": premises,
            "conclusion": conclusion,
            "is_valid": argument.is_valid,
            "explanation": argument.explanation
        }


# Example usage
if __name__ == "__main__":
    print("=== Deductive Reasoning Pattern Demo ===\n")
    
    pattern = DeductiveReasoningPattern()
    
    # Classic syllogism
    print("Example 1: Classic Syllogism")
    result1 = pattern.run(
        premises=[
            "All humans are mortal",
            "Socrates is a human"
        ],
        conclusion="Socrates is mortal"
    )
    print(f"Valid: {result1['is_valid']}")
    print(f"Explanation: {result1['explanation']}")
    
    # Modus ponens
    print("\n" + "="*60)
    print("Example 2: Modus Ponens")
    result2 = pattern.run(
        premises=[
            "If it rains, the ground gets wet",
            "It is raining"
        ],
        conclusion="The ground is wet"
    )
    print(f"Valid: {result2['is_valid']}")
    
    # Invalid argument
    print("\n" + "="*60)
    print("Example 3: Invalid Argument (Affirming the consequent)")
    result3 = pattern.run(
        premises=[
            "If it rains, the ground gets wet",
            "The ground is wet"
        ],
        conclusion="It rained"
    )
    print(f"Valid: {result3['is_valid']}")
    print(f"Explanation: {result3['explanation']}")
    
    print("\n✓ Deductive reasoning completed!")

Agentic Architecture Pattern: Deductive Reasoning

Pattern 203: 203 Deductive Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 203: Deductive Reasoning Pattern

Applies general rules to specific cases to draw logical conclusions. If premises are true, conclusion must be true (valid deduction).

Code Structure Analysis

File Information

Pattern Number: 203
Pattern Name: 203 Deductive Reasoning Pattern
File Path: patterns\advanced_reasoning\203_deductive_reasoning_pattern.py
Total Lines: 156

Detailed Code Explanation

Module Docstring

"""
Pattern 203: Deductive Reasoning Pattern

Applies general rules to specific cases to draw logical conclusions.
If premises are true, conclusion must be true (valid deduction).
"""

Explanation: This module docstring describes the purpose and key features of the 203 Deductive Reasoning Pattern pattern.

Imports Section

from typing import Dict, Any, List
from dataclasses import dataclass
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: Type hints for better code documentation – from dataclasses import dataclass: Data class decorators for clean data structures – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Classes

Premise (Line 15)

Description: A premise in deductive reasoning.

DeductiveArgument (Line 22)

Description: A deductive argument.

DeductiveReasoningPattern (Line 30)

Description: Deductive Reasoning Pattern – Logical inference from premises.

Applies formal logic: given premises, deduce valid conclusions. Checks validity of arguments.

Methods:

`init` (Line 38)

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”)

`apply_modus_ponens` (Line 42)

def apply_modus_ponens(self, conditional, antecedent):
    """Apply modus ponens: If P then Q, P is true, therefore Q."""

Purpose: Apply modus ponens: If P then Q, P is true, therefore Q.

Parameters: – self: Instance reference – conditional: Parameter for conditional – antecedent: Parameter for antecedent

`validate_argument` (Line 56)

def validate_argument(self, premises, conclusion):
    """Validate a deductive argument."""

Purpose: Validate a deductive argument.

Parameters: – self: Instance reference – premises: Parameter for premises – conclusion: Parameter for conclusion

`run` (Line 97)

def run(self, premises, conclusion):
    """Perform deductive reasoning."""

Purpose: Perform deductive reasoning.

Parameters: – self: Instance reference – premises: Parameter for premises – conclusion: Parameter for conclusion

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 = Premise(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: Abductive Reasoning


    """
Pattern 202: Abductive Reasoning Pattern

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

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


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


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


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


class AbductiveReasoningPattern:
    """
    Abductive Reasoning Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for abductive reasoning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: AbductiveReasoningConfig = None):
        self.config = config or AbductiveReasoningConfig()
        self.agents: Dict[str, AbductiveReasoningAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Abductive Reasoning Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: AbductiveReasoningAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Abductive Reasoning Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Abductive Reasoning Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


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

Agentic Architecture Pattern: Abductive Reasoning

Pattern 202: 202 Abductive Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 202: Abductive Reasoning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 202
Pattern Name: 202 Abductive Reasoning Pattern
File Path: patterns\advanced_reasoning\202_abductive_reasoning_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 202: Abductive Reasoning Pattern

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

Explanation: This module docstring describes the purpose and key features of the 202 Abductive 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Classes

AbductiveReasoningConfig (Line 18)

Description: Configuration for Abductive Reasoning Pattern.

AbductiveReasoningState (Line 27)

Description: State information for Abductive Reasoning Pattern.

AbductiveReasoningAgent (Line 36)

Description: Agent implementing Abductive Reasoning Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

def process(self, input_data):
    """Process input according to Abductive Reasoning Pattern."""

Purpose: Process input according to Abductive Reasoning Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

AbductiveReasoningPattern (Line 85)

Description: Abductive Reasoning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for abductive reasoning with full LangChain/LangGraph integration.

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

Methods:

`init` (Line 99)

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

Purpose: No method docstring.

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

`_build_workflow` (Line 105)

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

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

`register_agent` (Line 150)

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

Purpose: Register an agent with the pattern.

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

`execute` (Line 156)

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

Purpose: Execute the pattern with given input.

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

`run` (Line 188)

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

Purpose: Main execution method.

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

`get_statistics` (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = AbductiveReasoningConfig(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: Analogical Reasoning


    """
Pattern 201: Analogical Reasoning Pattern

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

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


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


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


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


class AnalogicalReasoningPattern:
    """
    Analogical Reasoning Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for analogical reasoning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: AnalogicalReasoningConfig = None):
        self.config = config or AnalogicalReasoningConfig()
        self.agents: Dict[str, AnalogicalReasoningAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Analogical Reasoning Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: AnalogicalReasoningAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Analogical Reasoning Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Analogical Reasoning Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


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

Agentic Architecture Pattern: Analogical Reasoning

Pattern 201: 201 Analogical Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 201: Analogical Reasoning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 201
Pattern Name: 201 Analogical Reasoning Pattern
File Path: patterns\advanced_reasoning\201_analogical_reasoning_pattern.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 201: Analogical Reasoning Pattern

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

Explanation: This module docstring describes the purpose and key features of the 201 Analogical 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Classes

AnalogicalReasoningConfig (Line 18)

Description: Configuration for Analogical Reasoning Pattern.

AnalogicalReasoningState (Line 27)

Description: State information for Analogical Reasoning Pattern.

AnalogicalReasoningAgent (Line 36)

Description: Agent implementing Analogical Reasoning Pattern.

Methods:

`init` (Line 39)

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

Purpose: No method docstring.

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

`process` (Line 49)

def process(self, input_data):
    """Process input according to Analogical Reasoning Pattern."""

Purpose: Process input according to Analogical Reasoning Pattern.

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

`get_statistics` (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

AnalogicalReasoningPattern (Line 85)

Description: Analogical Reasoning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for analogical reasoning with full LangChain/LangGraph integration.

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

Methods:

`init` (Line 99)

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

Purpose: No method docstring.

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

`_build_workflow` (Line 105)

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

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

`register_agent` (Line 150)

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

Purpose: Register an agent with the pattern.

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

`execute` (Line 156)

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

Purpose: Execute the pattern with given input.

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

`run` (Line 188)

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

Purpose: Main execution method.

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

`get_statistics` (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = AnalogicalReasoningConfig(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
Workflow Orchestration: LangGraph manages execution flow through nodes
State Management: TypedDict defines workflow state structure
Error Handling: Comprehensive error handling and fallback mechanisms
Extensibility: Designed for easy customization and extension

Extension Points

November 4, 2025

Tag: agentic

Agentic Architecture Pattern: Counterfactual Reasoning

Agentic Architecture Pattern: Counterfactual Reasoning

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

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Enumerations

CounterfactualType (Line 16)

Classes

CounterfactualType (Line 16)

ActualWorld (Line 25)

CounterfactualWorld (Line 33)

CounterfactualQuery (Line 43)

CounterfactualReasoningAgent (Line 50)

__init__ (Line 53)

generate_counterfactual (Line 58)

compare_worlds (Line 144)

identify_critical_factors (Line 172)

explore_minimal_changes (Line 201)

CounterfactualReasoningPattern (Line 240)

__init__ (Line 248)

register_agent (Line 251)

run (Line 255)

Design Patterns Used

Usage Example

Key Concepts

Extension Points

Agentic Architecture Pattern: Logical Reasoning

Agentic Architecture Pattern: Logical Reasoning

Pattern 209: 209 Logical Reasoning Pattern – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Enumerations

LogicalOperator (Line 15)

Classes

LogicalOperator (Line 15)

Proposition (Line 25)

LogicalRule (Line 33)

LogicalArgument (Line 41)

LogicalReasoningAgent (Line 49)

__init__ (Line 52)

add_proposition (Line 58)

evaluate_logical_expression (Line 63)

check_validity (Line 82)

construct_proof (Line 111)

apply_modus_ponens (Line 139)

check_consistency (Line 161)

solve_logic_puzzle (Line 182)

LogicalReasoningPattern (Line 197)

__init__ (Line 205)

register_agent (Line 208)

run (Line 212)

Design Patterns Used

Usage Example

Key Concepts

Extension Points

Agentic Architecture Pattern: Spatial Reasoning

Agentic Architecture Pattern: Spatial Reasoning

Pattern 208: 208 Spatial Reasoning Pattern – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Enumerations

SpatialRelation (Line 15)

Classes

SpatialRelation (Line 15)

SpatialObject (Line 32)

SpatialQuery (Line 41)

SpatialReasoningAgent (Line 48)

`init` (Line 53)

`generate_counterfactual` (Line 58)

`compare_worlds` (Line 144)

`identify_critical_factors` (Line 172)

`explore_minimal_changes` (Line 201)

`init` (Line 248)

`register_agent` (Line 251)

`run` (Line 255)

`init` (Line 52)

`add_proposition` (Line 58)

`evaluate_logical_expression` (Line 63)

`check_validity` (Line 82)

`construct_proof` (Line 111)

`apply_modus_ponens` (Line 139)

`check_consistency` (Line 161)

`solve_logic_puzzle` (Line 182)

`init` (Line 205)

`register_agent` (Line 208)

`run` (Line 212)

`init` (Line 51)

`add_object` (Line 56)

`determine_relation` (Line 60)

`calculate_distance` (Line 97)

`find_path` (Line 111)

`check_visibility` (Line 138)

`reason_about_scene` (Line 150)

`init` (Line 184)

`register_agent` (Line 187)

`run` (Line 191)

`init` (Line 50)

`add_event` (Line 56)

`infer_temporal_order` (Line 60)

`calculate_duration` (Line 82)

`check_temporal_consistency` (Line 98)

`plan_schedule` (Line 128)

`reason_about_deadline` (Line 153)

`init` (Line 180)

`register_agent` (Line 183)

`run` (Line 187)

`init` (Line 48)

`identify_cause` (Line 54)

`predict_effect` (Line 76)

`explain_mechanism` (Line 98)

`build_causal_chain` (Line 114)

`simulate_intervention` (Line 145)

`init` (Line 184)