LangGraph + Graphiti + Long Term Memory = Powerful Agentic Memory
AI Summary
Summary of Video: Creating a Multi-Agent Chatbot using Langraph
Introduction
- Quick tutorial on building a multi-agent chatbot.
- Focus on using Langraph, knowledge graph, and long-term memory.
Challenges with Static Knowledge Bases
- Static knowledge bases limit system adaptability.
- Issues arise with outdated or irrelevant responses when information changes.
- Comparison made to graph and relational databases.
Introducing Graffiti
- Graffiti builds dynamic temporal knowledge graphs.
- Ingests structured and unstructured data for evolving insights.
- Offers a demonstration: chatbot querying product data and creating knowledge episodes.
Chatbot Functionality
- Chatbot uses GPT4.1 Mini; designed to act as a helpful salesperson.
- Utilizes knowledge graph connections to enhance user interactions.
- Implements conversation logging and real-time data fetching.
Graffiti vs. Graphreg
- Graffiti provides dynamic memory; updates in real-time.
- Graphreg improves retrieval but relies on static knowledge.
- Graffiti ideal for contexts with evolving information (sales, customer service).
Code Setup
- Environment Setup: Install requirements.txt and import libraries.
- Logging Configuration: Establishes root logger for output.
- Database Initialization: Set up Neo4j client and build indices.
- Data Ingestion: Create functions to read and add product data from JSON files into the database.
- User Interaction: Define asynchronous tools to retrieve product data based on user queries.
- Chatbot Logic: Create state management for conversations and integrate user tracking.
- Display Interface: Set up to handle user inputs and display responses in real-time.
Applications
- Graffiti exhibits potential in practical applications, enhancing service in domains that require up-to-date knowledge management.
Conclusion
- The video emphasizes the difference between static and dynamic knowledge systems and the importance of adaptability in AI agents.