The problem
Vector search handles the similarity question. Production agents also need source freshness, authorization scope, invalidation, proof, and traceability.
Symptoms
Signals that the issue is happening in production, not just in a benchmark.
A vector store returns deleted or superseded content because the source changed.
Top-k retrieval works in tests but fails under multi-tenant or policy-heavy production traffic.
The app has to bolt freshness, auth, cache, and proof logic around every retrieval call.
The team wants to keep its database but make retrieval safer.
How KyroDB solves
KyroDB solves this at the runtime boundary before prompt assembly.
KyroDB is not a replacement for pgvector, Qdrant, Pinecone, or other vector databases.
It sits above retrieval and enforces context correctness before prompt assembly.
pgvector is the first-class onboarding path, and Qdrant is supported as a secondary connector.
Other stores can fit when connectors can enforce scope, filters, watermarks, and freshness semantics.
Implementation
Practical steps for teams already using an agent backend, vector store, or RAG pipeline.
- 01
Keep vector storage where it already belongs.
- 02
Put KyroDB on the server-side retrieval path between agent service and vector store.
- 03
Use connector-specific guarantees for filters, watermarks, mutations, and scoped deletion.
- 04
Return ContextPackets to the prompt builder instead of raw top-k chunks.
When not to use it
If your vector database use case is simple semantic search with no agent action, no changing source state, and no scope risk, a separate context layer may be optional.
Is KyroDB a vector database?
No. KyroDB is a context correctness runtime that sits above existing stores and retrieval backends.
Why not build this inside the application?
You can, but freshness, scope, proof, replay, and connector behavior are easy to duplicate inconsistently across agents and services.