DiamantAI
This blog post is a tutorial based on, and a simplified version of, the course “Long-Term Agentic Memory With LangGraph” by Harrison Chase and Andrew Ng on DeepLearning.AI.
Conclusion
We’ve now built an email agent that’s far more than a simple script. Like a skilled human assistant who grows more valuable over time, our agent builds a multi-faceted memory system:
- Semantic Memory: A knowledge base of facts about your work context, contacts, and preferences
- Episodic Memory: A collection of specific examples that guide decision-making through pattern recognition
- Procedural Memory: The ability to improve its own processes based on feedback and experience
This agent demonstrates how combining different types of memory creates an assistant that actually learns from interactions and gets better over time.
Imagine coming back from a two-week vacation to find that your AI assistant has not only kept your inbox under control but has done so in a way that reflects your priorities and communication style. The spam is gone, the urgent matters were flagged appropriately, and routine responses were handled so well that recipients didn’t even realize they were talking to an AI. That’s the power of memory-enhanced agents.
This is just a starting point! You can extend this agent with more sophisticated tools, persistent storage for long-term memory, fine-grained feedback mechanisms, and even collaborative capabilities that let multiple agents share knowledge while maintaining privacy boundaries.
