Comparison

Pamie vs vector databases for agent memory

Vector databases are useful retrieval infrastructure. Pamie is an MCP memory server: it gives agents a typed memory tool surface, local durable storage, search, lifecycle, auth, and backup workflows.

Vector search MCP tools Agent memory

The short version

Use a vector database when your main problem is semantic nearest-neighbor search over embeddings. Use Pamie when your problem is agent memory: saving, searching, updating, pinning, deleting, retaining, backing up, and exposing memory through MCP.

These are not mutually exclusive. Pamie can use optional local vector search, but it does not treat embeddings as the whole memory system.

Where vector databases stop

Embeddings are not policy A vector index can find similar text, but it does not define retention, pinning, lifecycle tiers, or access logs by itself.

Search is not an agent interface Agents still need safe tools for writing, updating, deleting, and inspecting memory without raw database access.

Ranking needs more than similarity Useful memory often depends on recency, importance, tier, access history, metadata, and exact keyword matches.

Operations matter Backups, restores, local ownership, auth boundaries, and deployment shape are part of a memory product.

What Pamie adds

Pamie stores memory in SQLite, provides FTS5 keyword search, supports metadata filters and snippets, and can optionally add local vector ranking. The MCP endpoint is Bearer-protected and exposes memory operations instead of arbitrary SQL or shell execution.

That makes Pamie a higher-level memory layer for agents, not just a place to put vectors.

Install Pamie What is MCP memory?