Is Memory Spine a vector database?

No. Memory Spine uses vector embeddings for semantic search, but it's a purpose-built agent memory system - not a general-purpose vector store. It combines vector similarity with FTS5 keyword matching, importance scoring, memory consolidation, knowledge graph construction, and 32 MCP tools. Think of it as the difference between a filing cabinet and an executive assistant who happens to own a filing cabinet.

Can I migrate from Pinecone or ChromaDB to Memory Spine?

Yes. Export your text content and metadata (you don't need the raw vectors - Memory Spine regenerates embeddings on import). Use the memory_batch_store MCP tool to batch-import. We've migrated corpora of 40K+ documents in under two minutes.

When should I use a vector database instead of Memory Spine?

If you need 100M+ vectors with sub-10ms latency at that scale, use Pinecone or Qdrant. If your use case is pure document retrieval or product recommendations with no agent context needs, a vector database is the right tool. Memory Spine is built for agent memory - conversations, importance scoring, cross-session context, multi-agent handoffs. Different problems, different tools.

How does Memory Spine's search latency compare to Pinecone?

At 55K vectors, Memory Spine averages 3.2ms (p50) and 8.1ms (p95). Pinecone averages 45ms (p50) on their Starter pod. But Pinecone scales to billions of vectors where Memory Spine caps around 500K per instance. At large scale, Pinecone wins on latency-per-vector. At agent-typical scales (under 200K), Memory Spine is significantly faster.

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