Different agent patterns require different index structures. HNSW (Hierarchical Navigable Small World) is ideal for low-latency, high-recall search, making it the default for interactive retrieval nodes in LangGraph. It builds layered proximity graphs that reduce search hops while keeping precision above 95 %. For larger, periodically updated datasets, IVF_FLAT or IVF_SQ8 provide a balance between speed and memory use by clustering vectors into centroids and searching only relevant partitions.
When agents stream new data continuously, DiskANN offers disk-backed scalability with near-memory performance. It’s well suited for long-running LangGraph systems where knowledge grows faster than RAM. Milvus lets you benchmark these indexes and switch among them without schema changes.
For most agent applications, a hybrid strategy works best: HNSW for real-time queries and IVF-based collections for background retrieval or archiving. This layered indexing keeps latency predictable while controlling cost, ensuring LangGraph workflows remain responsive even as data volume multiplies.