There is a paper called MemPalace that stores conversations verbatim instead of extracting facts from them. It scores 96.6% on LongMemEval. Most extraction-based approaches sit around 70-80%.

That got my attention.

BlazorMemory has always used AI extraction. You send a conversation, an LLM pulls out discrete facts ("User is a software engineer"), and those facts get stored as vector embeddings. It works well. But it is lossy by design. The LLM decides what matters, and sometimes it gets that wrong.

Verbatim mode skips the extraction step. The raw conversation chunk goes straight into storage with an embedding. Nothing is thrown away.

How it works

Smart mode (the original approach):

Conversation -> LLM extraction -> facts -> embeddings -> storage

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Verbatim mode (new):

Conversation -> embedding -> storage

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That is it.…