via MarkTechPost
The 7 Types of Agent Memory: A Technical Guide for AI Engineers
Large language models (LLMs) are inherently stateless. Each API call begins anew—once the response is returned, the model forgets your previous message. While acceptable for single queries, this limitation becomes critical when building agents.
Agents plan, call tools, and execute across multiple steps. They require memory. Memory is the infrastructure that bridges the gap, transforming a stateless model into a system that retains context, learns from experience, and acts over time. As of 2026, with agentic AI becoming mainstream, understanding memory types is essential for building robust, production-ready agents.
## What Is Agent Memory?
Memory refers to any mechanism that preserves information across a model’s reasoning cycles. Some memory resides within the context window; some lives externally—in databases, vector stores, or model weights. Each type stores distinct classes of information for varying durations, serving different purposes in an agent's lifecycle.