V3 Memory Unification

Migrate agent memory from SQLite and Markdown backends by consolidating disparate systems into a single AgentDB. The process uses batch migration tooling to generate 1536-d embeddings, producing a unified store for cross-agent semantic retrieval while preserving backward compatibility.

HNSW vector search for agent memory is a semantic retrieval mechanism using 1536-d embeddings to index unified data. It enables sub-100ms query latency targets for datasets containing over a million entries, dramatically accelerating agent retrieval workflows.

Yes, you can achieve sub-100ms semantic search latency with over a million entries by unifying disparate agent stores into AgentDB. This migration applies HNSW indexing with 1536-d embeddings to maintain high-throughput semantic retrieval at scale.

Yes, unifying agent memory systems preserves backward compatibility with legacy backends. The migration consolidates disparate stores like SQLite, Markdown, and Hybrid systems into AgentDB while maintaining access to existing SONA learning patterns and data.

The best way to consolidate hybrid and in-memory agent stores is migrating them into a unified AgentDB with HNSW indexing. This approach provides high-throughput migration tooling and generates 1536-d embeddings to enable cross-agent semantic retrieval and SONA learning storage.