A lightweight, stateless database for agent memory
I've been working on Polign and built a small prototype around something I've been thinking about with agent memory. I have built a lightweight/stateless vector db + BM25 search which works really well with typed facts and structured queries. It uses your own S3, or GCS bucket as primary storage, and restarting a node is fairly quick. Demo + writeup: https://polign.com/blog-edge-agent-memory Live search demo: https://demo.polign.com Docs: https://polign.com
In plain words
Polign is a lightweight, stateless vector database designed for agent memory that combines vector search and BM25 search for typed facts and structured queries. It stores data in users' own S3 or GCS buckets as primary storage, enabling quick node restarts and edge deployment. Built to address how agents represent and access memory reliably, it helps agents maintain accurate facts and preferences without forgetting or misquoting stored information.
written from the facts on this page · September 2026
From the sources
I've been thinking about agent memory for quite some time. There are really two things that bother me. The first is how we represent memory in the agent space. The second is where that memory should live when the agent is running on a hardware constraints. Somehow my work converged both of the concerns into a single answer. What I've observed in my own agent use is that there are certain behaviors and patterns that LLMs can't follow and sometimes miss or forget. I would correct a fact and the agent would quote the old version a week later. I would change a preference and recall would return both versions, and the model had to guess which one I meant. I realized something. Right now we make…from polign.com
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