V3 Memory Unification

You consolidate fragmented agent memory by migrating legacy SQLite, Markdown, and hybrid backends into a unified AgentDB backend. This replaces data silos with consistent cross-agent memory sharing and enables fast vector search.

HNSW indexing provides high-speed similarity search across stored memory entries within the AgentDB backend. It enables fast, scalable vector retrieval, allowing agents to quickly query relevant shared context across the unified memory service.

Yes, AgentDB supports backward compatibility when migrating from SQLite and Markdown stores. Data migration pipelines preserve existing metadata and embeddings, ensuring legacy systems remain accessible during the unification process.

Yes, you can integrate SONA learning patterns with a unified memory backend. The system supports SONA integration to enable collaborative AI workflows, allowing agents to share and learn from cross-agent memory efficiently.

Building a unified memory service with vector indexing requires satisfying ADR-006 for unified memory and ADR-009 for hybrid backends. You must implement embedding generation, HNSW indexing, and migration workflows to consolidate diverse stores.

The best way to migrate memory data while preserving embeddings is using dedicated migration pipelines. These pipelines transfer data from SQLite and Markdown to AgentDB while retaining existing metadata and generating necessary embeddings for HNSW indexing.