Beyondnearest neighbour.
Pinecone, Weaviate and pgvector return the most similar vectors. A graph returns entities and the relationships between them.
Nine rows.
Eight of them are a real difference. The ninth is the one they win.
A score is not a reason.
A vector index answers what looks like this. It is genuinely good at that, and for search over large volumes of prose it is still the better tool. What it cannot answer is what calls this, because similarity is not a relationship and a metadata filter is not an edge.
The gap shows up the moment a question has two steps in it. A top-k lookup is one round of nearest-neighbour search, so a second hop needs the first answer before it can even be phrased, and the model is left stitching disconnected chunks together. On a graph the second hop is one more edge.
And it goes stale differently.
Changing a file means re-embedding every chunk it touched before any of it can be retrieved again. Graphify re-parses that one file and updates its nodes; the edges around it stay as they were. The cost of keeping current is the size of the change, not the size of the repository.
Questions about the swap
What teams ask before they move.
As coding-assistant memory, yes: retrieval becomes a traversal over typed edges, so answers arrive connected and explainable. The products keep their place for workloads that live on pure similarity, and Graphify runs alongside any of them.
Yes. Some teams keep a vector index for semantic recall and use Graphify for structure. Graphify can also store embeddings as node attributes, so similarity is one signal inside a traversal.
It is a memory layer, not a database. Graphify builds the graph from your codebase and serves it over MCP and the CLI: no schema to model, no queries to write. If you already run Neo4j or FalkorDB, it exports straight into them.
Hybrid blends keyword and vector scores, and the result is still a ranked list of disconnected chunks. A graph returns connected entities, so the structure survives retrieval.