How Perplexity Memory Works: What It Remembers (and What It Doesn't)

Perplexity Memory is described as a cross-conversation system that retains recurring user preferences, interests, work context, and relevant search history, allowing responses to incorporate prior context even when users switch among models such as GPT-4o, Claude, and Gemini. It uses separate layers for stored personal memories and past searches, with repeated topics serving as the main signal for what is retained, while sensitive information such as health and financial details is intended to be filtered out and incognito sessions avoid memory collection. The text states that a February 2026 update for Pro and Max subscribers improved recall from 77% to 95% by storing fewer, more relevant memories, and it notes that users can manage, disable, delete, or recover memories within a 30-day window. It also contrasts memory systems with retrieval-augmented generation, arguing that RAG alone cannot maintain evolving user preferences or resolve conflicting context, before promoting Supermemory as an API for developers seeking persistent agent memory with benchmarked retrieval performance.

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