Agent Memory
Definition
Agent memory refers to systems that allow AI agents to store, retrieve, and use information across interactions, enabling persistent context and learning from past experiences.
Why It Matters
Without memory, AI agents are stateless - they forget everything between conversations. Agent memory enables continuity: an agent can remember your preferences, recall previous conversations, learn from past mistakes, and build up knowledge over time. This is essential for long-running tasks and personalized experiences.
How It Works
Agent memory typically includes multiple types: short-term memory (current conversation context), long-term memory (persistent storage in vector databases), episodic memory (specific past events and interactions), and semantic memory (factual knowledge). Systems like Mem0 and MemGPT provide frameworks for managing these memory types, often using embeddings and retrieval to surface relevant memories.
When to Use It
Implement agent memory when you need: (1) personalization across sessions, (2) learning from user feedback, (3) accumulating knowledge over time, or (4) handling tasks that span multiple sessions. For simple one-shot queries, memory adds unnecessary complexity. Start simple and add memory layers as your use case demands them.