🔧Context Management Workshop

📚 Introduction

This workshop teaches you to master Python Context Manager core concepts and practical techniques through 9 progressive modules, specifically designed for LLM application development.

  1. Basic Concepts

  2. LLM Session Manager

  3. Async Manager

  4. Smart Session

  5. Nested Managers

  6. MCP Implementation

  7. AsyncExitStack vs @asynccontextmanager

  8. Local MCP Integration

  9. Design Patterns Analysis

🎯 Learning Path

01. Basic Concepts (01_basic_concepts.py)

  • Context Manager fundamentals and working principles

  • @contextmanager decorator usage

  • setup -> use -> cleanup pattern

  • Basic timer and temporary configuration examples

Run: python 01_basic_concepts.py

02. LLM Session Manager (02_llm_session_manager.py)

  • LLM session lifecycle management

  • Token calculation and cost tracking

  • Exception handling and state recovery

  • Production-grade application examples

Run: python 02_llm_session_manager.py

03. Async Manager (03_async_manager.py)

  • @asynccontextmanager usage

  • GPU resource pool management

  • Async LLM session processing

  • Concurrent task coordination

Run: python 03_async_manager.py

04. Smart Session (04_smart_session.py)

  • contextvars global state management

  • Automatic session awareness mechanism

  • No need to manually pass parameters

  • Inspired by llamabot project

Run: python 04_smart_session.py

05. Nested Managers (05_nested_managers.py)

  • Multi-layer Context Manager nesting

  • Budget control + performance monitoring + session management

  • Enterprise-grade system resource management

  • Best practices for complex scenarios

Run: python 05_nested_managers.py

06. MCP Implementation (06_mcp_implementation.md)

  • Model Context Protocol detailed analysis

  • Enterprise-grade AI context management standards

  • Multi-transport layer support (stdio/SSE/WebSocket)

  • Advanced AsyncExitStack resource management patterns

  • Comparison analysis with llamabot and OpenAI SDK

Read: View 06_mcp_implementation.md document

07. AsyncExitStack vs @asynccontextmanager (07_asyncexitstack_vs_asynccontextmanager.py)

  • Detailed comparison of two async context management approaches

  • Single resource vs multiple resource management

  • Dynamic resource management and error handling

  • Best practices for combined usage

Run: python 07_asyncexitstack_vs_asynccontextmanager.py

08. Local MCP Integration (08_local_mcp_integration.py)

  • Real scenarios for local integration of multiple MCP services

  • Service orchestration for development and production environments

  • Necessity of Context Manager in MCP integration

  • Bad example vs good example comparison

Run: python 08_local_mcp_integration.py

09. Design Patterns Analysis (09_design_patterns_analysis.md)

  • In-depth analysis of design patterns embodied in Context Manager

  • Template Method, Builder, Composite and other pattern synergies

  • Pattern application value in enterprise-grade architectures

  • Design philosophy and practical guidance for multi-pattern collaboration

Read: View 09_design_patterns_analysis.md document

🚀 Quick Start

# Clone project
cd workshops/context_workshop

# Run all modules in sequence
python 01_basic_concepts.py
python 02_llm_session_manager.py
python 03_async_manager.py
python 04_smart_session.py
python 05_nested_managers.py

💡 Real Application Scenarios

  • File Operations: with open() as f

  • Database Connections: Session management and transaction control

  • LLM API Calls: Cost tracking and session management

  • GPU Resources: Async resource pool management

  • Temporary Configurations: Test environment configuration switching

  • Performance Monitoring: Automatic timing and resource statistics

🔗 Related Projects

  • llamabot: Uses PromptRecorder Context Manager to automatically record LLM conversations

  • OpenAI Agents SDK: RunContextWrapper manages LLM runtime context

  • Model Context Protocol (MCP): Anthropic's open standard for enterprise AI context management

  • FastMCP: Python framework for simplifying MCP server development

✅ Key Points

  1. Context Manager ensures proper resource acquisition and release

  2. yield statement separates setup and cleanup phases

  3. finally block guarantees cleanup code execution

  4. Async support suitable for high-concurrency and resource-intensive scenarios

  5. contextvars enables global state awareness and automatic context passing

  6. Nested composition provides powerful multi-layer resource management capabilities

  7. MCP protocol standardized enterprise-grade AI context management solution

  8. Design pattern synergy Context Manager is a typical example of elegant combination of multiple design patterns

These patterns are widely used in production by well-known projects like llamabot, OpenAI SDK, and Model Context Protocol!

🎯 Design Pattern Essence

Context Manager embodies the collaborative application of multiple classic design patterns:

  • Template Method - Defines fixed resource management algorithm skeleton (Setup → Use → Cleanup)

  • Builder Pattern - AsyncExitStack progressively builds complex resource structures

  • Composite Pattern - Nested structures form hierarchical resource management trees

  • Facade Pattern - Provides simplified unified interfaces for complex subsystems

  • Strategy Pattern - Different resource management strategies for different scenarios

  • Observer Pattern - Monitors and observes resource usage status

  • Factory Pattern - Dynamically creates appropriate resource managers

This multi-pattern synergy design philosophy is the core concept of modern enterprise software architecture!

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