Semantic search
Also called: vector search, embedding search, retrieval by meaning
What is semantic search?
Semantic search retrieves results by meaning rather than by matching words. Text is converted into a vector embedding, and a query finds the entries whose embeddings sit closest to it. That is why a search for authentication flow can return a memory written about the login handshake, which shares no words with the query.
How retrieval by meaning works.
Three steps, and the second is where most of the quality lives.
Text becomes a vector
An embedding model turns each entry into a list of numbers positioned so that similar meanings land near each other. This happens once, when the memory is written.
A query becomes a vector too
The search text goes through the same model, then the store returns the nearest entries by distance. Nothing is compared as literal text at any point.
Ranking decides what surfaces
Nearness alone is not enough in a corpus that has been accumulating for a year. Recency, importance, and usage shape what actually comes back first.
Why it matters for agent memory.
The alternative is requiring an agent to guess the exact words a different agent used months earlier.
Vocabulary stops mattering
Nobody has to remember the phrasing used when a memory was written, which is the failure mode keyword search hits immediately.
It works across authors
Different developers and different agents describe the same thing differently. Meaning-based retrieval is what makes a shared corpus usable.
It survives time
Terminology drifts as a codebase evolves. Embeddings degrade far more gracefully than exact-match rules.
Commonly confused with.
Three neighbouring ideas.
Semantic search questions
Why not just use keyword search?
Because the query and the stored memory are usually written by different agents at different times in different words. Exact matching fails precisely when a corpus has become large enough to be valuable.
Does semantic search return wrong results?
It returns the nearest matches, which is not the same as correct. That is why ranking beyond raw similarity matters, and why stale entries need to expire rather than linger.
What happens as the corpus grows?
Without maintenance, quality drops as old entries compete with current ones. Virex Memory refreshes relevance scoring on a schedule and expires stale memories automatically.
Can I search memories myself?
Yes. The portal has a browse and search surface, so the corpus is not only reachable by agents.
Does search cost extra?
No. A seat is $15 per seat per month, with no per-query or per-token component.
Retrieval that survives your vocabulary.
Virex Memory searches by meaning across everything your agents have written.