LLM Python Patterns

Python is an easy-to-use language for hands-on development but may not be as strictly structured as Java or C++ in enforcing coding rules and design patterns.

However, Python has become a powerful partner for LLM development, helping to build modern applications through frameworks such as LangChain, LangGraph, and LangExtract, and playing a significant role in modern application development.

Inspired by the python-patterns repo, this project demonstrates how classic design patterns solve real-world challenges in LLM applications. Through hands-on examples, enterprise case studies, and ready-to-use Claude Code templates, we explore practical implementations that enhance system robustness and maintainability.

We also aspire to enable Claude Code to systematically generate high-quality, pattern-based code by providing comprehensive templates and examples that can be intelligently combined for optimal AI system architecture.

📚 Documentation

📘 Complete Documentation Available: Visit GitBook documentation for comprehensive guides, tutorials, and detailed explanations of all design patterns and implementations.

Design Patterns Overview

Pattern Category	Pattern Name	Documentation	Project Link	LLM Application Focus
Behavioral	Chain of Responsibility	chain_of_responsibility_cases.md	-	Agent routing, request processing pipelines, multi-step reasoning workflows
	Command	-	-	Tool execution, operation history, undo/redo functionality, agent task encapsulation
	Iterator	-	-	Data streaming, batch processing, sequential AI model execution
	Mediator	-	-	Multi-agent communication, centralized coordination, system integration
	Memento	-	-	Conversation state management, checkpoint/restore, workflow rollback
	Observer	observer_cases.md	observer_pattern.ipynb	Real-time monitoring, cost tracking, performance analytics, system transparency, event-driven coordination, MCP server monitoring
	Strategy	strategy_cases.md	strategy_pattern.ipynb	Multi-provider selection, cost optimization, routing algorithms, dynamic model switching, plugin architectures, context management strategies
	Template Method	template_method_cases.md	-	Standardized workflows, document processing pipelines, agent behavior templates, consistent AI processing steps
	Visitor	-	-	AST processing, code analysis, hierarchical data traversal
Creational	Abstract Factory	abstract_factory_cases.md	factory_cases.ipynb	Multi-provider AI families, agent ecosystems, tool families, environment-specific components
	Builder	builder.md	-	Complex prompt construction, configurable pipelines, step-by-step AI workflows, flexible system configuration, MCP server construction
	Factory	factory_cases.md	-	Dynamic provider selection, agent creation, tool instantiation, runtime object creation
	Prototype	-	-	Agent template cloning, configuration duplication, rapid instance creation
	Singleton	-	-	Global configuration management, shared resources, cache coordination
Structural	Adapter	adapter_cases.md	-	Multi-provider integration, legacy system connectivity, data format standardization, protocol bridging, multi-transport abstraction
	Bridge	-	-	Platform abstraction, UI/logic separation, multi-environment deployment
	Composite	-	-	Hierarchical agent systems, nested workflows, tree-structured AI processing
	Decorator	decorator_cases.md	decorator_pattern.ipynb	Response caching, LLM enhancement layers, middleware, transparent functionality addition, AI-aware tool registration
	Facade	-	-	Unified interfaces, CLI simplification, complex system abstraction, developer-friendly APIs
	Flyweight	-	-	Memory optimization, shared configurations, efficient resource usage
	Proxy	proxy_cases.md	proxy_pattern.ipynb	Enterprise LLM gateways, access control, rate limiting, intelligent caching, security, cost optimization
Fundamental	Delegation	-	-	Responsibility delegation, task forwarding, capability distribution, modular system design
Other	Blackboard	-	-	Multi-agent knowledge sharing, collaborative reasoning, shared problem-solving workspace
	Graph Search	-	-	AI pathfinding, decision trees, state space exploration, workflow optimization
	Hierarchical State Machine (HSM)	-	-	Complex AI behavior modeling, state transitions, conversation flow management, agent lifecycle

Workshop Projects

1. AI Agent Chain: Chain of Responsibility Pattern in Action - ai_agent_chain_example.ipynb

Notebook demonstrating how to build intelligent AI agent workflows using classic design patterns, similar to LangChain and LangGraph architectures.

Implemented Patterns:

• Behavioral: Chain of Responsibility, Strategy, Template Method
• Creational: Abstract Factory (for different agent types)
• Structural: Adapter (for API client abstraction)

Goal: Build modular, scalable AI agent systems with proper separation of concerns

Similar Product Mindset: LangChain agent workflows, LangGraph state machines, OpenAI Assistant API

2. JSON Schema Factory + Pydantic Validation: Structured LLM Output Control - json_schema_factory_pydantic.ipynb

Workshop showing how to get structured JSON from LLMs using Factory Pattern and Pydantic validation.

Patterns Used:

• Factory Pattern - Choose correct schema based on data type
• Template Method - Standard prompt generation process

What it does:

• Takes any LLM output and validates it into clean JSON
• Works with OpenAI, Gemini, Anthropic, or any LLM provider
• Catches data errors before they enter your application
• Alternative to OpenAI Function Calling with more control

3. 🔧 Python Context Manager Workshop: Enterprise-Grade Resource Management - context_workshop/

Comprehensive 9-module workshop series demonstrating Context Manager mastery for LLM applications.

Workshop Modules:

• Basic Concepts - Core @contextmanager usage and principles
• LLM Session Manager - Production-grade session lifecycle management
• Async Manager - @asynccontextmanager for concurrent processing
• Smart Session - contextvars global state management
• Nested Managers - Multi-layer resource orchestration
• MCP Implementation - Model Context Protocol detailed analysis
• AsyncExitStack vs @asynccontextmanager - Advanced comparison
• Local MCP Integration - Real-world multi-service scenarios
• Design Patterns Analysis - Multi-pattern synergy and architectural insights

Key Learning: Context Manager embodies multiple design patterns working in harmony - Template Method, Builder, Composite, Facade, Strategy, Observer, and Factory patterns collaborate to create enterprise-grade resource management solutions.

Workshop Project Pattern Mapping

Workshop Project	Primary Patterns	Secondary Patterns	Focus Area	Link
AI Agent Chain	Chain of Responsibility, Strategy, Abstract Factory	Template Method, Adapter, Facade	Multi-agent workflows, request routing	ai_agent_chain_example.ipynb
JSON Schema Factory + Pydantic	Factory, Template Method	-	Structured LLM output control, provider-independent validation	json_schema_factory_pydantic.ipynb
Python Context Manager Workshop	Template Method, Builder, Composite	Facade, Strategy, Observer, Factory	Multi-pattern synergy, enterprise resource management, MCP integration	context_workshop/
Future Project	-	-	-	-

Enterprise Cases Analysis

Real-world production AI systems from leading companies, analyzing design patterns at enterprise scale.

Company Project	Primary Patterns	Supporting Patterns	Architecture Focus	Analysis Link	Structure Link
ByteDance Trae-Agent	Strategy, Command, Factory, Template Method	Observer, Facade, Registry, Configuration	Multi-LLM agent system, production-ready architecture	bytedance_trae_agent_analysis.md	tree_structure.md
Resume-Matcher	Strategy, Template Method, Factory, Observer	Command, Facade, Builder, Adapter	AI-powered document analysis, privacy-first local processing	resume_matcher_analysis.md	tree_structure.md
BerriAI LiteLLM	Adapter, Strategy, Factory, Proxy	Observer, Template Method, Decorator, Command	Enterprise LLM proxy, multi-provider abstraction, cost optimization	litellm_analysis.md	tree_structure.md
Prefect FastMCP	Decorator, Adapter, Proxy, Builder	Strategy, Observer, Template Method	Model Context Protocol framework, AI tool integration, multi-transport architecture	fastmcp_analysis.md	tree_structure.md
OpenManus FoundationAgents	Agent, Strategy, Facade, Factory	Command, Observer, Template Method, Adapter	Multi-agent AI framework, configurable agent orchestration, plugin architecture	openmanus_analysis.md	tree_structure.md
Future Analysis	-	-	-	-	-

Claude Code Templates

Ready-to-use templates and decision guides for building AI applications with design patterns. Perfect for Claude Code integration and rapid development.

🚀 Essential Decision Guide

Template Name	Design Patterns Used	Use Case	Template Link
🎯 LLM Pattern Decision Guide	All Patterns	Instant pattern selection, ROI insights, production templates	llm_pattern_decision_guide.md

📦 Project Templates

Template Name	Design Patterns Used	Use Case	Template Link
AI Agent System	Template Method, Factory, Chain of Responsibility, Strategy, Command	Multi-agent workflows, intelligent task routing, LangChain-style systems	ai_agent_system_template.md
Multi-LLM Provider	Abstract Factory, Strategy, Facade, Observer, Command	Cost-optimized LLM integration, provider failover, vendor independence	multi_llm_provider_template.md

💡 Start Here: Use the Decision Guide to quickly identify the right patterns for your needs, then implement with our production-tested templates.

Getting Started with Real LLM Testing

To test LLM in this project, you can set up free API access:

🚀 Quick Setup for Google AI Studio (Recommended)

1. Create Google Cloud Project: Visit Google Cloud Platform and create a new project
2. Get Free Credits: Google provides free credits for new users to test their AI services
3. Generate API Key: Go to Google AI Studio, click "Get API Key" and create a new key
4. Configure Environment:
   • Copy example.env to .env
   • Replace YOUR_GEMINI_API_KEY with your actual API key
   • Run the notebooks for real LLM testing!

📝 Environment Setup

# Copy the environment template
cp example.env .env

# Edit .env with your API keys
# The .env file is automatically ignored by git for security

Learning Notes 📝

Personal knowledge repository for insights, reflections, and detailed observations gathered while exploring Python design patterns in LLM applications.

📚 Organized Knowledge Collection

• Pattern-Based Notes - Basic fundamentals, Behavioral, Creational, and Structural pattern insights
• General Insights - Cross-pattern connections and observations
• News & Discoveries - Industry trends and technical breakthroughs

🎯 Quick Access

• Browse All Notes - Complete navigation and introduction
• Summary Index - Progress tracking and comprehensive overview

A streamlined personal space for capturing learning insights, pattern connections, and staying updated with industry developments in AI applications.

License

MIT License - see LICENSE for details.

Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/name
3. Make changes and test
4. Submit pull request

We welcome pattern implementations, documentation, and enterprise case studies!