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Self-Improving Memory for Agents

Self-Improving Memory for Agents

AI Perplexity
Perplexity Brain introduces persistent context graphs for AI agents, allowing them to recall past decisions and project data across sessions.
What: The system links memories to source documents and continuously updates knowledge, reducing the need for redundant work while keeping context relevant to ongoing tasks.
Why it matters: This addresses the 'stateless' nature of typical LLM interactions, moving agents toward long-term memory systems that can maintain continuity in complex, multi-day professional workflows.
Decoder
  • Context graph: A data structure representing relationships between different pieces of information, helping the model understand how separate facts, files, and decisions are connected.
Original article

Perplexity Brain is a memory system that builds a persistent context graph across tasks, projects, decisions, files, and sources so agents can start with relevant context instead of starting from scratch. Brain links every memory to its original source, continuously updates and organizes knowledge over time, and improves answer correctness while reducing task costs through better retrieval and reuse of prior work.

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