How do you make AI remember in production?
Store durable knowledge, retrieve the right context for each question, and write useful conversation state back into memory instead of replaying an entire transcript every time.
Supavector Agents Memory
Build AI agents that remember with durable agent memory
Supavector is an AI memory and agent memory platform for teams that need vector search, grounded retrieval, source sync, public chat, and production testing in one hosted workflow.
What this page covers
- Use Supavector as an agent memory layer, AI memory API, or vector database-backed retrieval system for assistants and apps.
- Works with LangChain, custom agent frameworks, backend workers, and server-side application stacks.
- Useful for teams comparing Pinecone, Supabase pgvector, or raw vector database setups when the real need is durable memory workflows instead of embeddings alone.
Common questions
What is the difference between a vector database and agent memory?
A vector database stores embeddings and similarity-search results. Agent memory adds sync, grounding, testing, public chat, and memory write and read workflows on top of retrieval.