Vector database alternative
MemexAI vs vector database memory.
Vector search is excellent for finding similar text. Persistent user memory needs something else too: a maintained record of preferences, decisions, constraints, corrections, and policies that people can inspect.
Core difference
Vector databases retrieve old text. MemexAI maintains what should survive.
MemexAI
Agents write durable memory into scoped files. The files live in Postgres, carry revision history, and can be inspected from the admin UI.
Vector memory
Store conversation chunks, embed them, retrieve similar fragments, and inject them into the prompt. Useful, but not the same as a maintained user record.
Technical tradeoff
Semantic similarity is not the same as memory state.
Question
MemexAI
Vector database
What does the agent read?
Curated files that represent current memory
Nearest chunks from an embedded archive
How are updates handled?
Patch or rewrite the durable record with revisions
Append/update source text and re-index embeddings
How do you remove stale facts?
Edit the file that owns the fact
Find affected chunks, delete/update them, re-index
Best fit
Current user model and behavior guidance
Transcript search, document search, and broad recall
Rule of thumb
Use both if you need both archive search and durable personalization.
Keep raw transcripts and semantic search where they belong. Use MemexAI for the smaller working set your AI should carry forward: preferences, constraints, decisions, timelines, and source-backed updates.