V3 Memory Unification

Unifying disparate agent memory stores involves migrating incompatible backends like SQLite, Markdown, and custom in-memory systems into a single AgentDB. This creates a common API for fast, consistent, and shareable semantic retrieval across agents.

Migrating legacy SQLite and Markdown memories requires robust data migration routines to convert legacy records and files into AgentDB. You must generate embeddings for the records and apply HNSW vector indexing to enable large-scale semantic search.

HNSW indexing improves vector search latency by optimizing embedding retrieval in AgentDB. This provides major speedups in query performance, allowing cross-agent semantic queries and pattern retrieval to run with sub-100ms latency on large datasets.

You can store and retrieve learning patterns across multiple agents by integrating SONA learning pattern storage into AgentDB. This supports reinforcement and adaptive behaviors by storing adaptation metadata within the unified memory backend.

AgentDB supports backward compatibility for existing agent workflows during memory unification. The migration process ensures existing systems like DistributedMemorySystem and SwarmMemory can transition to the unified backend without breaking current operations.

Setting up HNSW vector indexing for agent memory migration requires embeddings with specific dimensions, such as 1536. You need an AgentDB adapter that supports store and query operations to handle these embeddings during the data migration process.