What is AI Agent Memory?
AI agent memory is an application-level system that stores and retrieves relevant information, past interactions, or facts across multiple steps or chat sessions, allowing the agent to maintain context over time.
How does it work?
The language model itself usually remains stateless—it forgets everything between calls. Memory is implemented by the surrounding application. Possible memory types include:
- Working memory: Temporary storage for the current task.
- Conversation history: The transcript of recent messages.
- Episodic memory: Records of past events and interactions.
- Semantic memory: General facts and concepts retrieved from a vector database or knowledge graph.
- User preferences: Explicitly saved details about the user.
- Structured state: JSON or database records tracking the agent's progress.
What is a common misconception?
Do not describe the model’s context window as permanent memory. The context window is simply the maximum amount of text the model can process in a single request, and it is cleared for every new interaction unless the application re-injects the history.
Why does it matter?
Without memory, an agent would ask the same questions repeatedly and lose track of long-running goals. Robust memory systems enable personalized experiences and allow autonomous agents to operate effectively over extended periods.