V3 Memory Unification

You consolidate multiple agent memory systems by migrating them into a unified AgentDB backend. This approach uses HNSW vector indexing to deliver high-performance semantic recall and enables cross-agent memory sharing across all legacy systems.

Migrating SQLite memory data to a vector search backend requires dedicated migration tooling that transfers records into AgentDB. The workflow generates embeddings for your existing data and builds HNSW vector indexes to enable fast semantic querying.

Yes, you can use HNSW vector indexing for cross-agent memory sharing by unifying your data into AgentDB. The UnifiedMemoryService allows multiple agents to query a single backend and retrieve relevant memory patterns using fast semantic search.

Yes, AgentDB supports migrating memory data from Markdown files. The provided migration tooling parses your Markdown backends, generates vector embeddings for the text content, and loads them into the unified HNSW-indexed memory system.

Before unifying memory backends with AgentDB, you need an AgentDB adapter with HNSW support, an embedding generation service, and migration tooling. You must also prepare your existing data sources like SQLite databases or Markdown files for extraction.

Use HNSW indexing for agent memory retrieval to achieve high-performance semantic search across large datasets. Unlike standard database queries, HNSW vector indexing matches meaning and context, allowing agents to retrieve relevant memory patterns based on intent rather than exact string matches.