AI Memory

AI agents forget everything between sessions. Every conversation starts from zero—no context about who you are, what you’ve discussed, or what the assistant has learned. This is not just an implementation detail; it fundamentally limits what AI Agents can do.

There are many approaches to giving AI agents persisted memory so that when starting a new task, the agent is provided with the context it needs to be able to complete the task successfully. These range from storing information in a text-based markdown file, such as CLAUDE.md or AGENTS.md, which the agent is instructed to read at startup, to complex RAG (Retrieval-Augmented Generation) systems that are capable of consuming your entire corporate documentation system and giving the agent a ‘search index’.

Hindsight

For Errand AI, we have chosed to integrate with Hindsight.

We believe that text-file based memory systems are inefficient and wasteful since the entire file has to be loaded into the agent’s limited context window and will contain information that is not necessary for the completion of the task at hand.

Traditional RAG only retrieves documents similar to a query.

Hindsight provides structured memory with temporal reasoning, entity understanding, and belief formation.

Hindsight uses its own AI model for processing memories. For guidance on choosing the right model, see the Choosing the Right Models guide.

If you use the Errand Desktop app to install your Errand service, it will automatically download and configure Hindsight to work with your deployment.