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Agent Memory

The mechanism that lets an AI agent retain context across steps and sessions, split into short-term (within a task) and long-term (persisted across tasks) memory.

What Is Agent Memory?

A stateless LLM forgets everything between calls. Agent memory gives an agent continuity: short-term memory holds the current task scratchpad, while long-term memory persists facts, preferences, and past outcomes in a vector store or database the agent retrieves from later. Good memory design is what separates an agent that repeats its mistakes from one that improves. Poor design leaks stale or irrelevant context and degrades responses.

How Groovy Web Uses This

We design tiered memory for production agents, keeping short-term scratch in the prompt and long-term recall in a vector store, with retention policies so memory stays relevant rather than bloated.

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Our AI-First engineers build production systems using Agent Memory technology. Talk to us.

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