Tutorial 6: Memory Systems in LangChain

What you'll learn

1. Types of LangChain memory systems:

   • Buffer Memory
   • Summary Memory
   • Conversation Buffer/Summary Memory
   • Vector Store-Backed Memory
   • Knowledge Graph Memory

2. Implementing memory systems:

   • Basic conversation memory
   • Long-term memory with summaries
   • Combined memory approaches
   • Knowledge graph memory for complex relationships

Prerequisites

• Completion of Tutorials 1-5
• Python and Jupyter Notebooks
• Groq API key (https://console.groq.com)

Getting Started

1. Ensure Virtual Environment is Activated

Linux/macOS:

cd langchain-langgraph-tutorial
source venv/bin/activate
cd Tutorial06

Windows:

cd langchain-langgraph-tutorial
.\venv\Scripts\activate
cd Tutorial06

2. Launch Jupyter Notebook

jupyter notebook Tutorial_6_Memory_Systems_in_LangChain.ipynb

Components

Core Files

• Tutorial_6_Memory_Systems_in_LangChain.ipynb: Main tutorial notebook
• utils/: Helper functions for memory implementations
• examples/: Sample conversations and memory patterns

Key Features

Memory Types

• ConversationBufferMemory
• ConversationSummaryMemory
• CombinedMemory
• ConversationKGMemory

Example Applications

• Chat systems with memory
• Knowledge graph-based conversations
• Multi-modal memory implementations

Troubleshooting

Common Issues:

1. Memory initialization errors
2. Token limit management
3. Context preservation
4. Knowledge graph queries

Next Steps

After completing this tutorial:

1. Study memory persistence strategies
2. Experiment with custom memory implementations
3. Build advanced memory-aware applications

Stay tuned for Tutorial 7: Advanced Agent Patterns

Additional Resources

• LangChain Memory Documentation
• Knowledge Graph Implementation Guide
• Memory System Best Practices