Compared
Nearest is one signal. It is not the question.
Chroma’s README calls it "the open-source data infrastructure for AI" and shows a Python client plus a server mode. Qdrant’s README calls it "a vector similarity search engine and vector database" with a service, an API, and a cloud. Both answer which vectors are nearest.
What they say they are
Those lines are from the Chroma README and the Qdrant README , fetched 30 September 2026. LanceDB sits in the same category: embeddings, metadata, nearest neighbours. activate() uses vector neighbours as one of three signals, fused with full-text search and graph spread. Without a vector you supply, the fusion never sees one, and recall is lexical.
Approximate nearest-neighbour search past a large collection has not been evaluated here. That absence is stated with the project’s other limits on the catalogue . It is not a claim that a vector database stops working at the same point.
When to pick which
Pick a vector database when you are searching a corpus — documents, not an agent’s own experience — or you need a scale this project has not measured. Pick FluctlightDB when what you are storing is what an agent did and was told, and you want provenance, salience and a checkpoint. The short form is on the comparison .