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Agentic Design Pattern: Code Agent

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    """
Pattern 071: Code Agent Patterns

Description:
    Code Agent Patterns represent specialized agents designed for software development tasks
    including code analysis, generation, refactoring, debugging, testing, and documentation.
    These agents understand programming languages, design patterns, best practices, and can
    interact with codebases to assist developers. They combine static analysis, dynamic
    execution, code understanding, and generation capabilities to provide intelligent
    programming assistance.
    
    Code agents go beyond simple code completion to understand context, intent, architecture,
    and can reason about code quality, performance, security, and maintainability. They can
    explain existing code, suggest improvements, generate tests, refactor implementations,
    and even help with system design.

Components:
    1. Code Parser: Analyzes syntax and structure
    2. Code Generator: Creates new code based on specifications
    3. Code Analyzer: Identifies issues, patterns, and opportunities
    4. Code Refactorer: Improves code structure and quality
    5. Test Generator: Creates unit and integration tests
    6. Documentation Generator: Produces code documentation
    7. Debugger Assistant: Helps identify and fix bugs

Architecture:
    ```
    Code Task Request
        ↓
    Task Classifier → [Analyze/Generate/Refactor/Debug/Test/Document]
        ↓
    Code Understanding → [Parse, analyze structure & semantics]
        ↓
    Task Executor → [Execute specialized task]
        ↓
    Code Validator → [Check correctness & quality]
        ↓
    Result + Explanation
    ```

Use Cases:
    - Automated code review and quality assessment
    - Intelligent code completion and generation
    - Refactoring suggestions and automation
    - Bug detection and debugging assistance
    - Test generation and coverage improvement
    - Documentation generation and maintenance
    - Code translation between languages
    - Architecture and design assistance

Advantages:
    - Accelerates development workflow
    - Improves code quality and consistency
    - Reduces bugs and technical debt
    - Provides learning opportunities for developers
    - Automates repetitive coding tasks
    - Maintains coding standards automatically

LangChain Implementation:
    Uses ChatOpenAI for code understanding and generation. Demonstrates
    code analysis, generation, refactoring, test creation, and documentation.
"""

import os
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import re
from dotenv import load_dotenv

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

load_dotenv()


class CodeTaskType(Enum):
    """Types of code-related tasks."""
    ANALYZE = "analyze"
    GENERATE = "generate"
    REFACTOR = "refactor"
    DEBUG = "debug"
    TEST = "test"
    DOCUMENT = "document"
    REVIEW = "review"
    EXPLAIN = "explain"


class CodeLanguage(Enum):
    """Supported programming languages."""
    PYTHON = "python"
    JAVASCRIPT = "javascript"
    TYPESCRIPT = "typescript"
    JAVA = "java"
    CPP = "cpp"
    GO = "go"
    RUST = "rust"


class CodeQualityIssue(Enum):
    """Types of code quality issues."""
    BUG = "bug"
    SECURITY = "security"
    PERFORMANCE = "performance"
    STYLE = "style"
    COMPLEXITY = "complexity"
    DUPLICATION = "duplication"
    MAINTAINABILITY = "maintainability"


@dataclass
class CodeSnippet:
    """Represents a piece of code."""
    code: str
    language: CodeLanguage
    file_path: Optional[str] = None
    line_start: Optional[int] = None
    line_end: Optional[int] = None


@dataclass
class CodeIssue:
    """Represents a code quality issue."""
    issue_type: CodeQualityIssue
    severity: str  # "critical", "high", "medium", "low"
    description: str
    line_number: Optional[int] = None
    suggestion: Optional[str] = None


@dataclass
class CodeAnalysis:
    """Results of code analysis."""
    snippet: CodeSnippet
    issues: List[CodeIssue]
    complexity_score: float
    maintainability_score: float
    summary: str


@dataclass
class GeneratedCode:
    """Generated code with metadata."""
    code: str
    language: CodeLanguage
    explanation: str
    test_code: Optional[str] = None
    dependencies: List[str] = field(default_factory=list)


@dataclass
class RefactoredCode:
    """Refactored code with changes."""
    original: str
    refactored: str
    changes: List[str]
    improvements: List[str]


class CodeAgent:
    """
    Specialized agent for code-related tasks.
    """
    
    def __init__(self, default_language: CodeLanguage = CodeLanguage.PYTHON):
        """
        Initialize code agent.
        
        Args:
            default_language: Default programming language
        """
        self.default_language = default_language
        
        # Specialized LLMs for different tasks
        self.analyzer = ChatOpenAI(temperature=0.2, model="gpt-3.5-turbo")
        self.generator = ChatOpenAI(temperature=0.4, model="gpt-3.5-turbo")
        self.refactorer = ChatOpenAI(temperature=0.3, model="gpt-3.5-turbo")
        self.explainer = ChatOpenAI(temperature=0.3, model="gpt-3.5-turbo")
        
        self.parser = StrOutputParser()
    
    def analyze_code(self, snippet: CodeSnippet) -> CodeAnalysis:
        """
        Analyze code for quality issues.
        
        Args:
            snippet: Code to analyze
            
        Returns:
            Code analysis with issues and metrics
        """
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are a code quality expert. Analyze the code and identify issues.

Consider:
1. Bugs and potential errors
2. Security vulnerabilities
3. Performance issues
4. Code style and best practices
5. Complexity and maintainability
6. Code duplication

Provide:
Issues: [type:severity:line:description:suggestion, ...]
Complexity: [1-10 score]
Maintainability: [1-10 score]
Summary: [brief overall assessment]

Format each issue as: type:severity:line:description:suggestion"""),
            ("user", """Language: {language}
Code:
```
{code}
```

Analyze this code:""")
        ])
        
        chain = prompt | self.analyzer | self.parser
        
        try:
            result = chain.invoke({
                "language": snippet.language.value,
                "code": snippet.code
            })
            
            # Parse result
            issues = []
            complexity_score = 5.0
            maintainability_score = 5.0
            summary = ""
            
            for line in result.split('\n'):
                line = line.strip()
                if line.startswith("Issues:"):
                    issues_str = line.replace("Issues:", "").strip()
                    if issues_str.lower() not in ['none', '']:
                        # Parse issues
                        for issue_str in issues_str.split(','):
                            parts = issue_str.strip().split(':')
                            if len(parts) >= 5:
                                try:
                                    issue_type = CodeQualityIssue(parts[0].strip().lower())
                                except ValueError:
                                    issue_type = CodeQualityIssue.BUG
                                
                                issue = CodeIssue(
                                    issue_type=issue_type,
                                    severity=parts[1].strip(),
                                    description=parts[3].strip(),
                                    line_number=int(parts[2].strip()) if parts[2].strip().isdigit() else None,
                                    suggestion=parts[4].strip() if len(parts) > 4 else None
                                )
                                issues.append(issue)
                
                elif line.startswith("Complexity:"):
                    try:
                        complexity_score = float(re.search(r'\d+', line).group())
                    except:
                        pass
                
                elif line.startswith("Maintainability:"):
                    try:
                        maintainability_score = float(re.search(r'\d+', line).group())
                    except:
                        pass
                
                elif line.startswith("Summary:"):
                    summary = line.replace("Summary:", "").strip()
            
            if not summary:
                summary = "Code analysis completed."
            
            return CodeAnalysis(
                snippet=snippet,
                issues=issues,
                complexity_score=complexity_score,
                maintainability_score=maintainability_score,
                summary=summary
            )
            
        except Exception as e:
            print(f"Error analyzing code: {e}")
            return CodeAnalysis(
                snippet=snippet,
                issues=[],
                complexity_score=5.0,
                maintainability_score=5.0,
                summary="Analysis failed."
            )
    
    def generate_code(
        self,
        description: str,
        language: Optional[CodeLanguage] = None,
        include_tests: bool = False
    ) -> GeneratedCode:
        """
        Generate code from description.
        
        Args:
            description: What the code should do
            language: Target programming language
            include_tests: Whether to generate tests
            
        Returns:
            Generated code with explanation
        """
        language = language or self.default_language
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert programmer. Generate clean, efficient, well-documented code.

Follow best practices:
- Clear naming conventions
- Proper error handling
- Type hints (where applicable)
- Docstrings/comments
- Modular design

Provide:
Code: [complete implementation]
Explanation: [how it works]
Dependencies: [required libraries, comma-separated]
{test_instruction}"""),
            ("user", """Language: {language}
Task: {description}

Generate the code:""")
        ])
        
        test_instruction = ""
        if include_tests:
            test_instruction = "Tests: [unit tests for the code]"
        
        chain = prompt | self.generator | self.parser
        
        try:
            result = chain.invoke({
                "language": language.value,
                "description": description,
                "test_instruction": test_instruction
            })
            
            # Parse result
            code = ""
            explanation = ""
            test_code = None
            dependencies = []
            
            current_section = None
            section_lines = []
            
            for line in result.split('\n'):
                if line.startswith("Code:"):
                    if current_section:
                        self._save_section(current_section, section_lines, 
                                         locals())
                    current_section = "code"
                    section_lines = []
                elif line.startswith("Explanation:"):
                    if current_section:
                        self._save_section(current_section, section_lines,
                                         {"code": code})
                        code = '\n'.join(section_lines)
                    current_section = "explanation"
                    section_lines = [line.replace("Explanation:", "").strip()]
                elif line.startswith("Dependencies:"):
                    if current_section == "explanation":
                        explanation = '\n'.join(section_lines)
                    deps_str = line.replace("Dependencies:", "").strip()
                    if deps_str.lower() not in ['none', '']:
                        dependencies = [d.strip() for d in deps_str.split(',')]
                    current_section = "dependencies"
                    section_lines = []
                elif line.startswith("Tests:"):
                    if current_section == "code":
                        code = '\n'.join(section_lines)
                    current_section = "tests"
                    section_lines = []
                else:
                    section_lines.append(line)
            
            # Save last section
            if current_section == "code":
                code = '\n'.join(section_lines)
            elif current_section == "explanation":
                explanation = '\n'.join(section_lines)
            elif current_section == "tests":
                test_code = '\n'.join(section_lines)
            
            # Clean up code (remove markdown formatting)
            code = self._clean_code_block(code)
            if test_code:
                test_code = self._clean_code_block(test_code)
            
            if not explanation:
                explanation = "Generated code based on description."
            
            return GeneratedCode(
                code=code,
                language=language,
                explanation=explanation,
                test_code=test_code,
                dependencies=dependencies
            )
            
        except Exception as e:
            print(f"Error generating code: {e}")
            return GeneratedCode(
                code="# Error generating code",
                language=language,
                explanation="Code generation failed."
            )
    
    def _clean_code_block(self, code: str) -> str:
        """Remove markdown code block formatting."""
        # Remove ```language and ``` markers
        code = re.sub(r'^```\w*\n', '', code)
        code = re.sub(r'\n```$', '', code)
        code = code.strip()
        return code
    
    def _save_section(self, section: str, lines: List[str], context: Dict):
        """Helper to save parsed sections."""
        content = '\n'.join(lines)
        if section in context:
            context[section] = content
    
    def refactor_code(self, snippet: CodeSnippet, focus: str = "general") -> RefactoredCode:
        """
        Refactor code for better quality.
        
        Args:
            snippet: Code to refactor
            focus: Focus area (general, performance, readability, etc.)
            
        Returns:
            Refactored code with explanation
        """
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are a refactoring expert. Improve the code while preserving functionality.

Focus on:
- {focus}
- Code clarity and readability
- Best practices and patterns
- Performance optimization
- Maintainability

Provide:
Refactored: [improved code]
Changes: [list of changes made, comma-separated]
Improvements: [benefits of refactoring, comma-separated]"""),
            ("user", """Language: {language}
Original Code:
```
{code}
```

Refactor this code:""")
        ])
        
        chain = prompt | self.refactorer | self.parser
        
        try:
            result = chain.invoke({
                "language": snippet.language.value,
                "code": snippet.code,
                "focus": focus
            })
            
            # Parse result
            refactored = ""
            changes = []
            improvements = []
            
            for line in result.split('\n'):
                if line.startswith("Refactored:"):
                    # Start capturing refactored code
                    refactored_lines = []
                    continue
                elif line.startswith("Changes:"):
                    changes_str = line.replace("Changes:", "").strip()
                    if changes_str.lower() not in ['none', '']:
                        changes = [c.strip() for c in changes_str.split(',')]
                elif line.startswith("Improvements:"):
                    imp_str = line.replace("Improvements:", "").strip()
                    if imp_str.lower() not in ['none', '']:
                        improvements = [i.strip() for i in imp_str.split(',')]
                elif refactored == "" and not line.startswith("Changes") and not line.startswith("Improvements"):
                    refactored += line + '\n'
            
            refactored = self._clean_code_block(refactored)
            
            if not changes:
                changes = ["Code refactored"]
            if not improvements:
                improvements = ["Improved code quality"]
            
            return RefactoredCode(
                original=snippet.code,
                refactored=refactored if refactored else snippet.code,
                changes=changes,
                improvements=improvements
            )
            
        except Exception as e:
            print(f"Error refactoring code: {e}")
            return RefactoredCode(
                original=snippet.code,
                refactored=snippet.code,
                changes=["Refactoring failed"],
                improvements=[]
            )
    
    def explain_code(self, snippet: CodeSnippet, detail_level: str = "medium") -> str:
        """
        Explain what code does.
        
        Args:
            snippet: Code to explain
            detail_level: Level of detail (brief, medium, detailed)
            
        Returns:
            Explanation of the code
        """
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are a code educator. Explain code clearly and comprehensively.

Detail Level: {detail_level}

For brief: High-level summary in 2-3 sentences
For medium: Explain main components and flow
For detailed: Line-by-line or block-by-block explanation

Provide clear, educational explanation."""),
            ("user", """Language: {language}
Code:
```
{code}
```

Explain this code:""")
        ])
        
        chain = prompt | self.explainer | self.parser
        
        try:
            result = chain.invoke({
                "language": snippet.language.value,
                "code": snippet.code,
                "detail_level": detail_level
            })
            
            return result.strip()
            
        except Exception as e:
            print(f"Error explaining code: {e}")
            return "Unable to generate explanation."
    
    def generate_tests(self, snippet: CodeSnippet) -> str:
        """
        Generate unit tests for code.
        
        Args:
            snippet: Code to test
            
        Returns:
            Test code
        """
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are a testing expert. Generate comprehensive unit tests.

Include:
- Test for normal cases
- Edge cases
- Error handling
- Different input types

Use appropriate testing framework for the language."""),
            ("user", """Language: {language}
Code to Test:
```
{code}
```

Generate unit tests:""")
        ])
        
        chain = prompt | self.generator | self.parser
        
        try:
            result = chain.invoke({
                "language": snippet.language.value,
                "code": snippet.code
            })
            
            return self._clean_code_block(result)
            
        except Exception as e:
            print(f"Error generating tests: {e}")
            return "# Error generating tests"


def demonstrate_code_agent():
    """Demonstrate Code Agent pattern."""
    
    print("="*80)
    print("CODE AGENT PATTERNS - DEMONSTRATION")
    print("="*80)
    
    agent = CodeAgent(default_language=CodeLanguage.PYTHON)
    
    # Test 1: Code Analysis
    print("\n" + "="*80)
    print("TEST 1: Code Analysis")
    print("="*80)
    
    sample_code = """
def calculate_average(numbers):
    sum = 0
    for num in numbers:
        sum = sum + num
    return sum / len(numbers)
"""
    
    snippet1 = CodeSnippet(code=sample_code, language=CodeLanguage.PYTHON)
    analysis = agent.analyze_code(snippet1)
    
    print(f"\nComplexity Score: {analysis.complexity_score}/10")
    print(f"Maintainability Score: {analysis.maintainability_score}/10")
    print(f"\nSummary: {analysis.summary}")
    
    if analysis.issues:
        print(f"\nIssues Found: {len(analysis.issues)}")
        for issue in analysis.issues[:3]:
            print(f"  • {issue.issue_type.value.upper()} ({issue.severity}): {issue.description}")
            if issue.suggestion:
                print(f"    Suggestion: {issue.suggestion}")
    
    # Test 2: Code Generation
    print("\n" + "="*80)
    print("TEST 2: Code Generation")
    print("="*80)
    
    generated = agent.generate_code(
        description="Create a function to validate email addresses using regex",
        language=CodeLanguage.PYTHON,
        include_tests=True
    )
    
    print("\n--- Generated Code ---")
    print(generated.code[:300] + "..." if len(generated.code) > 300 else generated.code)
    
    print(f"\n--- Explanation ---")
    print(generated.explanation[:200] + "..." if len(generated.explanation) > 200 else generated.explanation)
    
    if generated.dependencies:
        print(f"\n--- Dependencies ---")
        print(", ".join(generated.dependencies))
    
    # Test 3: Code Refactoring
    print("\n" + "="*80)
    print("TEST 3: Code Refactoring")
    print("="*80)
    
    messy_code = """
def process(data):
    result = []
    for i in range(len(data)):
        if data[i] > 0:
            result.append(data[i] * 2)
    return result
"""
    
    snippet2 = CodeSnippet(code=messy_code, language=CodeLanguage.PYTHON)
    refactored = agent.refactor_code(snippet2, focus="readability")
    
    print("\n--- Original Code ---")
    print(refactored.original)
    
    print("\n--- Refactored Code ---")
    print(refactored.refactored)
    
    print("\n--- Changes Made ---")
    for change in refactored.changes:
        print(f"  • {change}")
    
    print("\n--- Improvements ---")
    for improvement in refactored.improvements:
        print(f"  • {improvement}")
    
    # Test 4: Code Explanation
    print("\n" + "="*80)
    print("TEST 4: Code Explanation")
    print("="*80)
    
    complex_code = """
def fibonacci(n, memo={}):
    if n in memo:
        return memo[n]
    if n  400 else tests)
    
    # Summary
    print("\n" + "="*80)
    print("SUMMARY: Code Agent Patterns")
    print("="*80)
    print("""
The Code Agent pattern demonstrates specialized AI assistance for software development:

Key Features Demonstrated:
1. Code Analysis: Identifies bugs, security issues, performance problems
2. Code Generation: Creates functions from natural language descriptions
3. Code Refactoring: Improves code structure and readability
4. Code Explanation: Provides clear educational explanations
5. Test Generation: Creates comprehensive unit tests
6. Quality Metrics: Assesses complexity and maintainability
7. Multi-language Support: Works across programming languages

Benefits:
• Accelerates development workflow significantly
• Improves code quality and consistency
• Reduces bugs and technical debt
• Provides learning opportunities for developers
• Automates repetitive coding tasks
• Maintains coding standards automatically
• Assists with code reviews and documentation

Use Cases:
• Automated code review in CI/CD pipelines
• Intelligent IDE code completion and suggestions
• Refactoring legacy code for maintainability
• Bug detection and debugging assistance
• Test generation for improving coverage
• Documentation generation and maintenance
• Code translation between languages
• Learning and education for new developers

Production Considerations:
• Implement actual static analysis tools integration
• Add support for project-wide context and dependencies
• Implement incremental analysis for large codebases
• Add caching for repeated analysis tasks
• Integrate with version control for change analysis
• Implement security scanning and vulnerability detection
• Add performance profiling and optimization suggestions
• Support for framework-specific best practices
• Integration with existing dev tools and IDEs
• Human review for critical code changes
• Metrics tracking for code quality improvements
• Team-specific style guide enforcement

Advanced Extensions:
• Multi-file refactoring with dependency tracking
• Architectural pattern detection and suggestions
• Automated bug fixing with verification
• Code clone detection and deduplication
• Performance optimization through profiling
• Security vulnerability patching
• API usage pattern analysis
• Code smell detection and remediation
    """)


if __name__ == "__main__":
    demonstrate_code_agent()

Agentic Design Pattern: Code Agent

Pattern 071: Code Agent – Line-by-Line Explanation

Overview

This document provides a detailed line-by-line explanation of the Code Agent pattern implementation.

Pattern Description

Pattern 071: Code Agent Patterns

Description: Code Agent Patterns represent specialized agents designed for software development tasks including code analysis, generation, refactoring, debugging, testing, and documentation. These agents understand programming languages, design patterns, best practices, and can interact with codebases to assist developers. They combine static analysis, dynamic execution, code understanding, and generation capabilities to provide intelligent programming assistance.

Code agents go beyond simple code completion to understand context, intent, architecture,
and can reason about code quality, performance, security, and maintainability. They can
explain existing code, suggest improvements, generate tests, refactor implementations,
and even help with system design.

Components: 1. Code Parser: Analyzes syntax and structure 2. Code Generator: Creates new code based on specifications 3. Code Analyzer: Identifies issues, patterns, and opportunities 4. Code Refactorer: Improves code structure and quality 5. Test Generator: Creates unit and integration tests 6. Documentation Generator: Produces code documentation 7. Debugger Assistant: Helps identify and fix bugs

Architecture: Code Task Request ↓ Task Classifier → [Analyze/Generate/Refactor/Debug/Test/Document] ↓ Code Understanding → [Parse, analyze structure & semantics] ↓ Task Executor → [Execute specialized task] ↓ Code Validator → [Check correctness & quality] ↓ Result + Explanation

Use Cases: – Automated code review and quality assessment – Intelligent code completion and generation – Refactoring suggestions and automation – Bug detection and debugging assistance – Test generation and coverage improvement – Documentation generation and maintenance – Code translation between languages – Architecture and design assistance

Advantages: – Accelerates development workflow – Improves code quality and consistency – Reduces bugs and technical debt – Provides learning opportunities for developers – Automates repetitive coding tasks – Maintains coding standards automatically

LangChain Implementation: Uses ChatOpenAI for code understanding and generation. Demonstrates code analysis, generation, refactoring, test creation, and documentation.

File Statistics

  • Total Lines: 802
  • Classes: 9
  • Functions: 1
  • Constants: 0

Module Documentation

"""
Pattern 071: Code Agent Patterns

Description:
    Code Agent Patterns represent specialized agents designed for software development tasks
    including code analysis, generation, refactoring, debugging, testing, and documentation.
    These agents understand programming languages, design patterns, best practices, and can
    interact with codebases to assist developers. They combine static analysis, dynamic
    execution, code understanding, and generation capabilities to provide intelligent
    programming assistance.

    Code agents go beyond simple code completion to understand context, intent, architecture,
    and can reason about code quality, performance, security, and maintainability. They can
    explain existing code, suggest improvements, generate tests, refactor implementations,
    and even help with system design.

Components:
    1. Code Parser: Analyzes syntax and structure
    2. Code Generator: Creates new code based on specifications
    3. Code Analyzer: Identifies issues, patterns, and opportunities
    4. Code Refactorer: Improves code structure and quality
    5. Test Generator: Creates unit and integration tests
    6. Documentation Generator: Produces code documentation
    7. Debugger Assistant: Helps identify and fix bugs

Architecture:
    ```
    Code Task Request
        ↓
    Task Classifier → [Analyze/Generate/Refactor/Debug/Test/Document]
        ↓
    Code Understanding → [Parse, analyze structure & semantics]
        ↓
    Task Executor → [Execute specialized task]
        ↓
    Code Validator → [Check correctness & quality]
        ↓
    Result + Explanation
    ```

Use Cases:
    - Automated code review and quality assessment
    - Intelligent code completion and generation
    - Refactoring suggestions and automation
    - Bug detection and debugging assistance
    - Test generation and coverage improvement
    - Documentation generation and maintenance
    - Code translation between languages
    - Architecture and design assistance

Advantages:
    - Accelerates development workflow
    - Improves code quality and consistency
    - Reduces bugs and technical debt
    - Provides learning opportunities for developers
    - Automates repetitive coding tasks
    - Maintains coding standards automatically

LangChain Implementation:
    Uses ChatOpenAI for code understanding and generation. Demonstrates
    code analysis, generation, refactoring, test creation, and documentation.
"""

Purpose: This module implements the Code Agent pattern, providing a structured approach to agent design and implementation.

Import Statements

Standard Library Imports

import dataclasses.dataclass
import dataclasses.field
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import re
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
import typing.Tuple

Purpose: Core Python functionality: – dataclasses.dataclass: Simplified class definitions with @dataclass – dataclasses.field: Simplified class definitions with @dataclass – enum.Enum: Enumeration support – langchain_core.output_parsers.StrOutputParser: Regular expression operations – langchain_core.prompts.ChatPromptTemplate: Regular expression operations – os: Environment variable access and system operations – re: Regular expression operations – typing.Any: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional) – typing.List: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Tuple: Type hints for better code documentation (List, Dict, Any, Optional)

LangChain Imports

from langchain_openai.ChatOpenAI

Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows

Other Imports

from dotenv.load_dotenv

Purpose: Additional dependencies for specialized functionality.

Classes

Class: CodeTaskType

Lines 78-87

Inherits from: Enum

Purpose:

Types of code-related tasks.

Class: CodeLanguage

Lines 90-98

Inherits from: Enum

Purpose:

Supported programming languages.

Class: CodeQualityIssue

Lines 101-109

Inherits from: Enum

Purpose:

Types of code quality issues.

Class: CodeSnippet

Lines 113-119

Purpose:

Represents a piece of code.

Class: CodeIssue

Lines 123-129

Purpose:

Represents a code quality issue.

Class: CodeAnalysis

Lines 133-139

Purpose:

Results of code analysis.

Class: GeneratedCode

Lines 143-149

Purpose:

Generated code with metadata.

Class: RefactoredCode

Lines 153-158

Purpose:

Refactored code with changes.

Class: CodeAgent

Lines 161-604

Purpose:

Specialized agent for code-related tasks.

Methods:

__init__(self, default_language)

Lines 166-181

Purpose:

Initialize code agent.

Args:
    default_language: Default programming language

Parameters: – default_language: Default language

analyze_code(self, snippet)

Lines 183-291

Purpose:

Analyze code for quality issues.

Args:
    snippet: Code to analyze

Returns:
    Code analysis with issues and metrics

Parameters: – snippet: Snippet

Returns: CodeAnalysis

generate_code(self, description, language, include_tests)

Lines 293-415

Purpose:

Generate code from description.

Args:
    description: What the code should do
    language: Target programming language
    include_tests: Whether to generate tests

Returns:
    Generated code with explanation

Parameters: – description: Description – language: Language – include_tests: Include tests

Returns: GeneratedCode

_clean_code_block(self, code)

Lines 417-423

Purpose:

Remove markdown code block formatting.

Parameters: – code: Code

Returns: str

_save_section(self, section, lines, context)

Lines 425-429

Purpose:

Helper to save parsed sections.

Parameters: – section: Section – lines: Lines – context: Context

refactor_code(self, snippet, focus)

Lines 431-516

Purpose:

Refactor code for better quality.

Args:
    snippet: Code to refactor
    focus: Focus area (general, performance, readability, etc.)

Returns:
    Refactored code with explanation

Parameters: – snippet: Snippet – focus: Focus

Returns: RefactoredCode

explain_code(self, snippet, detail_level)

Lines 518-561

Purpose:

Explain what code does.

Args:
    snippet: Code to explain
    detail_level: Level of detail (brief, medium, detailed)

Returns:
    Explanation of the code

Parameters: – snippet: Snippet – detail_level: Detail level

Returns: str

generate_tests(self, snippet)

Lines 563-604

Purpose:

Generate unit tests for code.

Args:
    snippet: Code to test

Returns:
    Test code

Parameters: – snippet: Snippet

Returns: str

Functions

demonstrate_code_agent()

Lines 607-797

Purpose:

Demonstrate Code Agent pattern.

Key Concepts Summary

Pattern Overview

The Code Agent pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.

Core Components

  1. Agent Class: Main implementation of the pattern logic
  2. Prompt Engineering: Carefully crafted prompts to guide LLM behavior
  3. Tool Integration: Optional tools for extended functionality
  4. Memory Management: State handling across interactions
  5. Output Parsing: Structured response processing

When to Use

✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability

❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)

Best Practices Demonstrated

1. Environment Configuration

  • ✅ Use .env files for API keys
  • ✅ Never hardcode credentials
  • ✅ Validate environment variables on startup

2. Error Handling

  • ✅ Graceful degradation on failures
  • ✅ Informative error messages
  • ✅ Logging for debugging

3. Prompt Engineering

  • ✅ Clear, specific instructions
  • ✅ Structured output formats
  • ✅ Few-shot examples when beneficial

4. Code Organization

  • ✅ Separation of concerns (class-based design)
  • ✅ Type hints for clarity
  • ✅ Docstrings for documentation

5. Performance Optimization

  • ✅ Appropriate temperature settings
  • ✅ Token budget management
  • ✅ Caching where applicable

LangChain Components Used

  • ChatOpenAI: OpenAI model interface
  • Chains: Sequential component composition

Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development

Execution Flow

1. Initialize agent with configuration
   ↓
2. Load environment and dependencies
   ↓
3. Prepare prompt with input data
   ↓
4. Invoke LLM through LangChain
   ↓
5. Parse and validate response
   ↓
6. Return formatted result

Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation

Performance Considerations

Computational Cost

  • LLM Calls: Primary cost factor (based on tokens)
  • Latency: Depends on model and input size
  • Memory: Minimal for most patterns

Optimization Strategies

  1. Prompt Optimization: Minimize tokens while maintaining clarity
  2. Caching: Cache repeated queries/responses
  3. Model Selection: Use smaller models when appropriate
  4. Batch Processing: Group similar requests
  5. Streaming: For long responses, stream tokens

Cost Estimates (Approximate)

  • GPT-3.5-turbo: ~$0.002 per request
  • GPT-4: ~$0.03 per request
  • GPT-4-turbo: ~$0.01 per request

Actual costs vary based on input/output token counts

Common Pitfalls to Avoid

1. Prompt Design Issues

❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts

2. Error Handling

❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation

3. Token Management

❌ Exceeding model token limits
✅ Truncate inputs, chunk processing

4. Security

❌ Exposing API keys in code
✅ Use environment variables

5. Testing

❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs

6. Cost Management

❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring

Further Improvements

Production Enhancements

  1. Monitoring: Add logging, metrics, tracing
  2. Validation: Input/output schema validation
  3. Rate Limiting: Implement request throttling
  4. Caching: Redis or in-memory cache for responses
  5. Async Support: Non-blocking operations for scalability

Feature Extensions

  1. Multi-Model Support: Switch between providers
  2. Fallback Models: Graceful degradation strategy
  3. Custom Tools: Domain-specific function calling
  4. Advanced Memory: Vector stores for long-term memory
  5. Evaluation: Automated testing and benchmarking

Code Quality

  1. Testing: Unit tests, integration tests
  2. Documentation: API docs, usage examples
  3. Type Safety: Stricter type hints
  4. Configuration: Externalized config files

Related Patterns

  • Pattern 070
  • Pattern 072

Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…

Summary

The Code Agent pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:

✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns

🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability

📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/

File: 071_code_agent.py
Total Lines: 802
Pattern Category: AI Agent Design Pattern #071

Generated from actual implementation code
For educational and reference purposes

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