Polign

What it does

Polign is a vector database designed for AI agent memory. It combines vector search with BM25 full-text search and enforces typed schemas for stored facts. The database is stateless—it stores nothing permanently on the server itself. Instead, it uses object storage like Amazon S3 or Google Cloud Storage as the primary backend. Queries and writes go through a lightweight server that can restart without data loss.

Who it is for

The tool targets builders running AI agents on resource-constrained devices or edge hardware. It is built for scenarios where agents need reliable memory but cannot rely on persistent infrastructure. Typical users would be those deploying agents in environments with cold-start requirements or unpredictable network conditions.

Pricing

The site does not show prices.

How it stands out

Polign makes two core design choices. First, it treats memory as typed and deterministic rather than semantic. Instead of relying on an LLM to interpret recalled facts, the database enforces schema rules for contradictions and updates. The creator argues this avoids token waste and accuracy loss from semantic retrieval of ambiguous results. Second, it is designed to run with minimal resident memory. A demonstration serves 12.5 million passages from S3 while consuming only 37 MiB of RAM on the server. The entire stack, including embedding generation and web interface, fits on a 2 GB ARM machine.

What a founder should check

Three things warrant investigation before building a competitor. First, verify whether the typed-schema approach actually solves the stated LLM memory problems better than semantic retrieval in real-world agent workflows. The creator's observations about models misremembering facts or returning conflicting versions are anecdotal. Second, test the switching cost for users already managing agent memory with existing vector databases like Pinecone or Weaviate. Moving schemas and reindexing large corpora could be friction. Third, examine the moat. Object storage as a backend is commodity infrastructure, and hybrid search (vector plus BM25) is well-understood. The durability claim—restarting without data loss—is achievable with other databases too. Long-term defensibility depends on whether typed schemas for agent memory becomes a standard expectation or remains a niche preference.