V3 Memory Unification

Unifying memory backends involves consolidating disparate memory systems into a single AgentDB-backed platform. This centralizes storage behind one interface, allowing transparent indexing and querying across all previously isolated memory sources.

Migrating legacy SQLite and Markdown stores preserves backward compatibility by maintaining existing access patterns. The process imports data directly into AgentDB, generating fresh embeddings on the fly while keeping older workflows functional.

HNSW indexing accelerates semantic search across unified memory by optimizing vector retrieval. It delivers 150x to 12,500x faster query performance compared to traditional methods, enabling real-time cross-agent sharing and updates.

Cross-agent memory sharing with SONA learning integration enables multiple agents to access a unified memory pool. It facilitates adaptive learning patterns that improve recall and decision-making based on shared usage data.

Migrating a 100k-entry memory corpus into AgentDB requires initializing a 1536-dimension HNSW index. You then transfer existing SQLite and Markdown data into the unified service while generating embeddings on the fly.

Initializing the HNSW index requires defining a specific vector dimension size, such as 1536 dimensions. This configuration must match the output dimensions of the embeddings generated during the memory data migration process.