Perplexity Details Brain Memory Architecture and Agent API Testing

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Perplexity on November 2, 2026, published a technical guide to persistent AI agent memory, detailing the Brain system in Perplexity Computer, the Agent API’s memory-building components, and tests for whether stored context improves an agent’s later work.

Brain is Perplexity’s self-improving memory system for Computer, built to organize past work and corrections into an evolving knowledge wiki. It uses token-level memory, meaning information is stored outside the model and read into context when needed. Changing a wiki page alters what the agent can read on a later task without modifying the model’s learned weights.

Brain stores these records as files in the agent’s sandbox across three layers. The sessions/ directory retains raw transcripts and their summaries, notes/ organizes distilled information by subject, and knowledge/ contains the wiki’s linked Markdown pages about people, projects and concepts. The guide classifies session records as episodic memory and the wiki as semantic memory; Skills and saved instructions provide procedural memory for how tasks should be performed.

Background agents called Dream process each day’s session records overnight, produce summaries and revise the wiki when information has changed. Staged edits are checked before they are committed through Git, leaving a diff available for inspection. Each memory entry links back to its source, while Brain’s background updates reinforce information that remains valid, revise entries after changes and mark outdated material.

Perplexity reported that Brain-enabled sessions in a 30-day paired evaluation improved correctness by 9.3 percentage points and recall by 8.9, while using roughly 15% fewer tokens. In a separate internal benchmark covering 150 financial-document questions, an exploration agent using standard file tools reached an answer score of 0.627, compared with about 0.37 for semantic search over the same corpus.