Separating RAG from memory can make AI agents handle context better
Retrieval and memory are related in AI agents, but they solve different problems. RAG is mainly about finding which context looks useful for the current answer. Memory also needs to track whether something was true at the time, whether it later became outdated, whether it should fade away because it was never important, and whether it should be kept because it explains a decision.
This matters more when the data is not a fixed set of documents, but changing work context from people, projects, messages, calendars, tasks, and decisions. OpenLoomi is a local-first open-source memory layer being used to test this separation.
Key points
- RAG focuses on finding context that seems relevant right now.
- Memory also needs to handle time, staleness, importance, and decision history.
- Changing workplace data makes this harder than searching fixed documents.
- A better memory layer may reduce token use by keeping unnecessary context out of prompts.
- OpenLoomi is a local-first open-source memory layer testing this idea.
Quick term guide
- retrieval
- The step where a system finds the most relevant text for a question.
- AI agents
- AI agents are AI tools that can carry out steps toward a goal, not just answer once.
- AI agent
- An AI program that can inspect information and suggest what to do next.
- context
- The information an AI uses to understand your request, such as files, notes, and past messages.
- local-first
- An app design where your data is mainly stored and controlled on your own device.
- open-source
- Software whose code is shared publicly so others can inspect, use, or change it.
- framing
- The way a story is presented so readers focus on one meaning or angle.
- implementation
- The step where a plan is turned into working code.