Perplexity has launched Brain, a new memory system designed to help its Computer agents improve from previous work rather than simply remember user preferences.
The system builds a context graph of the work performed by Computer. At set intervals, such as overnight, Brain reviews that graph and updates the agent’s working context so it can handle future tasks more efficiently.
Perplexity said the goal is to move AI memory beyond personalization. Most memory systems focus on the user, including preferences, contacts, work style and recurring instructions. Brain is focused on what the agent did, what worked, what failed and what corrections were made.
That distinction matters because agent memory is becoming a core part of AI product competition. A system that can learn from prior work can reduce repeated setup, avoid dead sources and start new tasks with a clearer sense of what the user is trying to accomplish.
Brain stores this context through what Perplexity describes as a living context graph. The context layer takes the form of an LLM wiki that is automatically loaded into the agent sandbox, giving Computer a map of the user’s projects, files, sources, people and prior sessions.
