Memory & State Management for Agents
Implement persistent memory and state management techniques for LLM agents to enable long-term conversations and complex task sequences.
Agents Need Memory
LLM agents are designed to perform complex tasks. But by default, LLMs are stateless! This means they don't "remember" past interactions.
For an agent to have a meaningful conversation or complete multi-step tasks, it needs a way to recall previous information. This is where memory comes in.
Short & Long-Term Memory
Agent memory can be categorized into two main types:
- Short-Term Memory: This is like a human's working memory. It holds recent, relevant information for immediate use, often the raw conversation history.
- Long-Term Memory: Stores information over extended periods, like past conversations or learned facts. It helps agents retain knowledge beyond the current interaction.
All lessons in this course
- Designing Multi-Agent Systems
- Memory & State Management for Agents
- Autonomous Workflow Automation
- Agent Reflection & Self-Correction Loops