On October 3, 2026, a detailed analysis on liao.gg highlighted that current AI memory plugins, which generate and retrieve snippets from conversation transcripts, fail to provide true understanding for AI agents. These plugins rely on retrieval-augmented generation (RAG) techniques, inserting the top five relevant snippets per prompt, but this approach does not enable agents to grasp the full context of projects.
The article explains that memory plugins operate by processing session transcripts into isolated snippets stored in vector databases. Each user prompt triggers retrieval of the most similar snippets, sometimes supplemented by manual searches when the agent is confused. Despite enhancements like multi-tier memory systems or background processes to merge and deduplicate data, the core architecture remains centered on RAG, which the author argues is misaligned with the actual needs of AI agents.
This critique matters because it challenges the prevailing design of AI memory systems, which aim to help agents remember past interactions. Instead, the author contends that agents require comprehensive documentation that clearly explains project features, intentions, and agreements. This distinction could influence future AI development, shifting focus from fragmented memory snippets to structured, accessible documentation for better agent comprehension.
The article concludes that the entire memory plugin ecosystem is solving the wrong problem, emphasizing that AI agents do not need memory as currently implemented but rather need documentation. This perspective was published on October 3, 2026, on liao.gg.