Edge 409: Augmenting Autonomous Agents with Long-Term Memory

Making agents remember beyond the context window.

Created Using Ideogram

In this Issue:

1. Different types of long-term memory in autonomous agents.

2. Microsoft’s LONGMEM research to enable long-term memory in LLMs.

3. The Pinecone vector database platform.

💡 ML Concept of the Day: Long-Term Memory and Autonomous Agents

In the previous issue of this series we discussed short-term memory in autonomous agents fundamentally powered by LLM context-windows. That approach has clear limitations as context-windows are finite, expensive to use and they only last in the context of a conversation. Long-term memory represents a natural complement to short-term memory by persisting information in a way that can be used beyond the lifecycle of a conversation.

The most common form of storage for long-term memory is vector databases but pretty much any form of durable storage can be adapted for this scenario. From a conceptual standpoint, there are three fundamental forms of long-term memory in autonomous agents: